Overview

Dataset statistics

Number of variables111
Number of observations39717
Missing cells2263364
Missing cells (%)51.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 MiB
Average record size in memory888.0 B

Variable types

Numeric25
Categorical19
Text11
Boolean2
Unsupported54

Alerts

pymnt_plan has constant value ""Constant
initial_list_status has constant value ""Constant
collections_12_mths_ex_med has constant value ""Constant
policy_code has constant value ""Constant
application_type has constant value ""Constant
acc_now_delinq has constant value ""Constant
chargeoff_within_12_mths has constant value ""Constant
delinq_amnt has constant value ""Constant
tax_liens has constant value ""Constant
id is highly overall correlated with member_id and 1 other fieldsHigh correlation
member_id is highly overall correlated with id and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
installment is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
delinq_2yrs is highly overall correlated with mths_since_last_delinqHigh correlation
mths_since_last_delinq is highly overall correlated with delinq_2yrs and 1 other fieldsHigh correlation
mths_since_last_record is highly overall correlated with id and 5 other fieldsHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
total_acc is highly overall correlated with open_accHigh correlation
out_prncp is highly overall correlated with out_prncp_inv and 1 other fieldsHigh correlation
out_prncp_inv is highly overall correlated with out_prncp and 1 other fieldsHigh correlation
total_pymnt is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly overall correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly overall correlated with loan_amnt and 7 other fieldsHigh correlation
recoveries is highly overall correlated with collection_recovery_fee and 1 other fieldsHigh correlation
collection_recovery_fee is highly overall correlated with recoveries and 1 other fieldsHigh correlation
last_pymnt_amnt is highly overall correlated with total_rec_prncp and 1 other fieldsHigh correlation
term is highly overall correlated with total_rec_int and 1 other fieldsHigh correlation
grade is highly overall correlated with sub_gradeHigh correlation
sub_grade is highly overall correlated with gradeHigh correlation
loan_status is highly overall correlated with out_prncp and 2 other fieldsHigh correlation
pub_rec is highly overall correlated with mths_since_last_record and 1 other fieldsHigh correlation
next_pymnt_d is highly overall correlated with mths_since_last_record and 5 other fieldsHigh correlation
pub_rec_bankruptcies is highly overall correlated with mths_since_last_record and 1 other fieldsHigh correlation
loan_status is highly imbalanced (51.4%)Imbalance
pub_rec is highly imbalanced (86.6%)Imbalance
next_pymnt_d is highly imbalanced (89.9%)Imbalance
pub_rec_bankruptcies is highly imbalanced (83.7%)Imbalance
emp_title has 2459 (6.2%) missing valuesMissing
emp_length has 1075 (2.7%) missing valuesMissing
desc has 12940 (32.6%) missing valuesMissing
mths_since_last_delinq has 25682 (64.7%) missing valuesMissing
mths_since_last_record has 36931 (93.0%) missing valuesMissing
next_pymnt_d has 38577 (97.1%) missing valuesMissing
mths_since_last_major_derog has 39717 (100.0%) missing valuesMissing
annual_inc_joint has 39717 (100.0%) missing valuesMissing
dti_joint has 39717 (100.0%) missing valuesMissing
verification_status_joint has 39717 (100.0%) missing valuesMissing
tot_coll_amt has 39717 (100.0%) missing valuesMissing
tot_cur_bal has 39717 (100.0%) missing valuesMissing
open_acc_6m has 39717 (100.0%) missing valuesMissing
open_il_6m has 39717 (100.0%) missing valuesMissing
open_il_12m has 39717 (100.0%) missing valuesMissing
open_il_24m has 39717 (100.0%) missing valuesMissing
mths_since_rcnt_il has 39717 (100.0%) missing valuesMissing
total_bal_il has 39717 (100.0%) missing valuesMissing
il_util has 39717 (100.0%) missing valuesMissing
open_rv_12m has 39717 (100.0%) missing valuesMissing
open_rv_24m has 39717 (100.0%) missing valuesMissing
max_bal_bc has 39717 (100.0%) missing valuesMissing
all_util has 39717 (100.0%) missing valuesMissing
total_rev_hi_lim has 39717 (100.0%) missing valuesMissing
inq_fi has 39717 (100.0%) missing valuesMissing
total_cu_tl has 39717 (100.0%) missing valuesMissing
inq_last_12m has 39717 (100.0%) missing valuesMissing
acc_open_past_24mths has 39717 (100.0%) missing valuesMissing
avg_cur_bal has 39717 (100.0%) missing valuesMissing
bc_open_to_buy has 39717 (100.0%) missing valuesMissing
bc_util has 39717 (100.0%) missing valuesMissing
mo_sin_old_il_acct has 39717 (100.0%) missing valuesMissing
mo_sin_old_rev_tl_op has 39717 (100.0%) missing valuesMissing
mo_sin_rcnt_rev_tl_op has 39717 (100.0%) missing valuesMissing
mo_sin_rcnt_tl has 39717 (100.0%) missing valuesMissing
mort_acc has 39717 (100.0%) missing valuesMissing
mths_since_recent_bc has 39717 (100.0%) missing valuesMissing
mths_since_recent_bc_dlq has 39717 (100.0%) missing valuesMissing
mths_since_recent_inq has 39717 (100.0%) missing valuesMissing
mths_since_recent_revol_delinq has 39717 (100.0%) missing valuesMissing
num_accts_ever_120_pd has 39717 (100.0%) missing valuesMissing
num_actv_bc_tl has 39717 (100.0%) missing valuesMissing
num_actv_rev_tl has 39717 (100.0%) missing valuesMissing
num_bc_sats has 39717 (100.0%) missing valuesMissing
num_bc_tl has 39717 (100.0%) missing valuesMissing
num_il_tl has 39717 (100.0%) missing valuesMissing
num_op_rev_tl has 39717 (100.0%) missing valuesMissing
num_rev_accts has 39717 (100.0%) missing valuesMissing
num_rev_tl_bal_gt_0 has 39717 (100.0%) missing valuesMissing
num_sats has 39717 (100.0%) missing valuesMissing
num_tl_120dpd_2m has 39717 (100.0%) missing valuesMissing
num_tl_30dpd has 39717 (100.0%) missing valuesMissing
num_tl_90g_dpd_24m has 39717 (100.0%) missing valuesMissing
num_tl_op_past_12m has 39717 (100.0%) missing valuesMissing
pct_tl_nvr_dlq has 39717 (100.0%) missing valuesMissing
percent_bc_gt_75 has 39717 (100.0%) missing valuesMissing
pub_rec_bankruptcies has 697 (1.8%) missing valuesMissing
tot_hi_cred_lim has 39717 (100.0%) missing valuesMissing
total_bal_ex_mort has 39717 (100.0%) missing valuesMissing
total_bc_limit has 39717 (100.0%) missing valuesMissing
total_il_high_credit_limit has 39717 (100.0%) missing valuesMissing
annual_inc is highly skewed (γ1 = 30.9491846)Skewed
collection_recovery_fee is highly skewed (γ1 = 25.02941842)Skewed
id has unique valuesUnique
member_id has unique valuesUnique
url has unique valuesUnique
mths_since_last_major_derog is an unsupported type, check if it needs cleaning or further analysisUnsupported
annual_inc_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
dti_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
verification_status_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_coll_amt is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_cur_bal is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_acc_6m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_6m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_rcnt_il is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bal_il is an unsupported type, check if it needs cleaning or further analysisUnsupported
il_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_rv_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_rv_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
max_bal_bc is an unsupported type, check if it needs cleaning or further analysisUnsupported
all_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_rev_hi_lim is an unsupported type, check if it needs cleaning or further analysisUnsupported
inq_fi is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_cu_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
inq_last_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_open_past_24mths is an unsupported type, check if it needs cleaning or further analysisUnsupported
avg_cur_bal is an unsupported type, check if it needs cleaning or further analysisUnsupported
bc_open_to_buy is an unsupported type, check if it needs cleaning or further analysisUnsupported
bc_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_old_il_acct is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_old_rev_tl_op is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_rcnt_rev_tl_op is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_rcnt_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
mort_acc is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_bc is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_bc_dlq is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_inq is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_revol_delinq is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_accts_ever_120_pd is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_actv_bc_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_actv_rev_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_bc_sats is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_bc_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_il_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_op_rev_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_rev_accts is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_rev_tl_bal_gt_0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_sats is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_120dpd_2m is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_30dpd is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_90g_dpd_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_op_past_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
pct_tl_nvr_dlq is an unsupported type, check if it needs cleaning or further analysisUnsupported
percent_bc_gt_75 is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_hi_cred_lim is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bal_ex_mort is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bc_limit is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_il_high_credit_limit is an unsupported type, check if it needs cleaning or further analysisUnsupported
delinq_2yrs has 35405 (89.1%) zerosZeros
inq_last_6mths has 19300 (48.6%) zerosZeros
mths_since_last_delinq has 443 (1.1%) zerosZeros
mths_since_last_record has 670 (1.7%) zerosZeros
revol_bal has 994 (2.5%) zerosZeros
out_prncp has 38577 (97.1%) zerosZeros
out_prncp_inv has 38577 (97.1%) zerosZeros
total_rec_late_fee has 37671 (94.8%) zerosZeros
recoveries has 35499 (89.4%) zerosZeros
collection_recovery_fee has 35935 (90.5%) zerosZeros

Reproduction

Analysis started2023-10-02 08:00:49.698683
Analysis finished2023-10-02 08:01:28.820224
Duration39.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683131.91
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:28.983228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile372418.4
Q1516221
median665665
Q3837755
95-th percentile1039966.2
Maximum1077501
Range1022767
Interquartile range (IQR)321534

Descriptive statistics

Standard deviation210694.13
Coefficient of variation (CV)0.30842379
Kurtosis-0.7298894
Mean683131.91
Median Absolute Deviation (MAD)160026
Skewness0.078807632
Sum2.713195 × 1010
Variance4.4392018 × 1010
MonotonicityNot monotonic
2023-10-02T15:01:29.044553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1077501 1
 
< 0.1%
568534 1
 
< 0.1%
568659 1
 
< 0.1%
567165 1
 
< 0.1%
568531 1
 
< 0.1%
568623 1
 
< 0.1%
568605 1
 
< 0.1%
556021 1
 
< 0.1%
568568 1
 
< 0.1%
568525 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
ValueCountFrequency (%)
54734 1
< 0.1%
55742 1
< 0.1%
57245 1
< 0.1%
57416 1
< 0.1%
58915 1
< 0.1%
59006 1
< 0.1%
61390 1
< 0.1%
61419 1
< 0.1%
62102 1
< 0.1%
65426 1
< 0.1%
ValueCountFrequency (%)
1077501 1
< 0.1%
1077430 1
< 0.1%
1077175 1
< 0.1%
1076863 1
< 0.1%
1075358 1
< 0.1%
1075269 1
< 0.1%
1072053 1
< 0.1%
1071795 1
< 0.1%
1071570 1
< 0.1%
1070078 1
< 0.1%

member_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850463.56
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:29.103959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile388192.4
Q1666780
median850812
Q31047339
95-th percentile1269461.8
Maximum1314167
Range1243468
Interquartile range (IQR)380559

Descriptive statistics

Standard deviation265678.31
Coefficient of variation (CV)0.31239235
Kurtosis-0.56296801
Mean850463.56
Median Absolute Deviation (MAD)190427
Skewness-0.21241637
Sum3.3777861 × 1010
Variance7.0584963 × 1010
MonotonicityNot monotonic
2023-10-02T15:01:29.168942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1296599 1
 
< 0.1%
731393 1
 
< 0.1%
731544 1
 
< 0.1%
729629 1
 
< 0.1%
731390 1
 
< 0.1%
731500 1
 
< 0.1%
731481 1
 
< 0.1%
716015 1
 
< 0.1%
731438 1
 
< 0.1%
731382 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
ValueCountFrequency (%)
70699 1
< 0.1%
73673 1
< 0.1%
74724 1
< 0.1%
76583 1
< 0.1%
80353 1
< 0.1%
80364 1
< 0.1%
84914 1
< 0.1%
85483 1
< 0.1%
86999 1
< 0.1%
89243 1
< 0.1%
ValueCountFrequency (%)
1314167 1
< 0.1%
1313524 1
< 0.1%
1311748 1
< 0.1%
1311441 1
< 0.1%
1306957 1
< 0.1%
1306721 1
< 0.1%
1305201 1
< 0.1%
1305008 1
< 0.1%
1304956 1
< 0.1%
1304884 1
< 0.1%

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11219.444
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:29.233164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7456.6707
Coefficient of variation (CV)0.66462035
Kurtosis0.76866855
Mean11219.444
Median Absolute Deviation (MAD)5000
Skewness1.0593173
Sum4.4560265 × 108
Variance55601938
MonotonicityNot monotonic
2023-10-02T15:01:29.297179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2833
 
7.1%
12000 2334
 
5.9%
5000 2051
 
5.2%
6000 1908
 
4.8%
15000 1895
 
4.8%
20000 1626
 
4.1%
8000 1586
 
4.0%
25000 1390
 
3.5%
4000 1130
 
2.8%
3000 1030
 
2.6%
Other values (875) 21934
55.2%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 301
0.8%
1050 4
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 679
1.7%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34200 1
 
< 0.1%
34000 15
 
< 0.1%
33950 9
 
< 0.1%
33600 6
 
< 0.1%
33500 2
 
< 0.1%

funded_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1041
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10947.713
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:29.356532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7187.2387
Coefficient of variation (CV)0.65650593
Kurtosis0.93755199
Mean10947.713
Median Absolute Deviation (MAD)4600
Skewness1.0817102
Sum4.3481032 × 108
Variance51656400
MonotonicityNot monotonic
2023-10-02T15:01:29.417997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2741
 
6.9%
12000 2244
 
5.6%
5000 2040
 
5.1%
6000 1898
 
4.8%
15000 1784
 
4.5%
8000 1573
 
4.0%
20000 1456
 
3.7%
25000 1133
 
2.9%
4000 1127
 
2.8%
3000 1022
 
2.6%
Other values (1031) 22699
57.2%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 302
0.8%
1050 5
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 554
1.4%
34800 1
 
< 0.1%
34675 2
 
< 0.1%
34525 1
 
< 0.1%
34475 4
 
< 0.1%
34250 1
 
< 0.1%
34000 14
 
< 0.1%
33950 6
 
< 0.1%
33600 6
 
< 0.1%
33500 1
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

HIGH CORRELATION 

Distinct8205
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10397.449
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:29.478023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1873.658
Q15000
median8975
Q314400
95-th percentile24736.572
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7128.4504
Coefficient of variation (CV)0.6855961
Kurtosis1.0625444
Mean10397.449
Median Absolute Deviation (MAD)4200
Skewness1.1062129
Sum4.1295548 × 108
Variance50814806
MonotonicityNot monotonic
2023-10-02T15:01:29.539540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1309
 
3.3%
10000 1275
 
3.2%
6000 1200
 
3.0%
12000 1069
 
2.7%
8000 900
 
2.3%
4000 812
 
2.0%
3000 803
 
2.0%
15000 657
 
1.7%
7000 600
 
1.5%
2000 452
 
1.1%
Other values (8195) 30640
77.1%
ValueCountFrequency (%)
0 129
0.3%
0.000121098 1
 
< 0.1%
0.000531133 1
 
< 0.1%
0.000654607 1
 
< 0.1%
0.001867696 1
 
< 0.1%
0.001963093 1
 
< 0.1%
0.001966974 1
 
< 0.1%
0.002251738 1
 
< 0.1%
0.002283598 1
 
< 0.1%
0.002373058 1
 
< 0.1%
ValueCountFrequency (%)
35000 135
0.3%
34997.35245 1
 
< 0.1%
34993.65539 1
 
< 0.1%
34993.32571 1
 
< 0.1%
34993.26306 1
 
< 0.1%
34993.19696 1
 
< 0.1%
34990.4308 1
 
< 0.1%
34987.98452 1
 
< 0.1%
34987.27101 1
 
< 0.1%
34977.34674 1
 
< 0.1%

term
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
36 months
29096 
60 months
10621 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months 29096
73.3%
60 months 10621
 
26.7%

Length

2023-10-02T15:01:29.595092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:29.641604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
months 39717
50.0%
36 29096
36.6%
60 10621
 
13.4%

Most occurring characters

ValueCountFrequency (%)
79434
20.0%
6 39717
10.0%
m 39717
10.0%
o 39717
10.0%
n 39717
10.0%
t 39717
10.0%
h 39717
10.0%
s 39717
10.0%
3 29096
 
7.3%
0 10621
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238302
60.0%
Space Separator 79434
 
20.0%
Decimal Number 79434
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 39717
16.7%
o 39717
16.7%
n 39717
16.7%
t 39717
16.7%
h 39717
16.7%
s 39717
16.7%
Decimal Number
ValueCountFrequency (%)
6 39717
50.0%
3 29096
36.6%
0 10621
 
13.4%
Space Separator
ValueCountFrequency (%)
79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 238302
60.0%
Common 158868
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 39717
16.7%
o 39717
16.7%
n 39717
16.7%
t 39717
16.7%
h 39717
16.7%
s 39717
16.7%
Common
ValueCountFrequency (%)
79434
50.0%
6 39717
25.0%
3 29096
 
18.3%
0 10621
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79434
20.0%
6 39717
10.0%
m 39717
10.0%
o 39717
10.0%
n 39717
10.0%
t 39717
10.0%
h 39717
10.0%
s 39717
10.0%
3 29096
 
7.3%
0 10621
 
2.7%
Distinct371
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:29.901178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6942871
Min length5

Characters and Unicode

Total characters226160
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row10.65%
2nd row15.27%
3rd row15.96%
4th row13.49%
5th row12.69%
ValueCountFrequency (%)
10.99 956
 
2.4%
13.49 826
 
2.1%
11.49 825
 
2.1%
7.51 787
 
2.0%
7.88 725
 
1.8%
7.49 656
 
1.7%
11.71 607
 
1.5%
9.99 603
 
1.5%
7.90 582
 
1.5%
5.42 573
 
1.4%
Other values (361) 32577
82.0%
2023-10-02T15:01:30.232795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146726
64.9%
Other Punctuation 79434
35.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38195
26.0%
9 21893
14.9%
2 12734
 
8.7%
7 12132
 
8.3%
6 12033
 
8.2%
4 11091
 
7.6%
5 9947
 
6.8%
3 9929
 
6.8%
8 9527
 
6.5%
0 9245
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 39717
50.0%
% 39717
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 226160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

installment
Real number (ℝ)

HIGH CORRELATION 

Distinct15383
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.56192
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:30.321337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.246
Q1167.02
median280.22
Q3430.78
95-th percentile762.996
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.76

Descriptive statistics

Standard deviation208.87487
Coefficient of variation (CV)0.64355939
Kurtosis1.2468013
Mean324.56192
Median Absolute Deviation (MAD)123.2
Skewness1.1284191
Sum12890626
Variance43628.713
MonotonicityNot monotonic
2023-10-02T15:01:30.385250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 68
 
0.2%
180.96 59
 
0.1%
311.02 54
 
0.1%
150.8 48
 
0.1%
368.45 46
 
0.1%
372.12 45
 
0.1%
330.76 43
 
0.1%
339.31 42
 
0.1%
301.6 41
 
0.1%
317.72 41
 
0.1%
Other values (15373) 39230
98.8%
ValueCountFrequency (%)
15.69 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.74 1
< 0.1%
21.81 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
 
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 5
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

grade
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B
12020 
A
10085 
C
8098 
D
5307 
E
2842 
Other values (2)
1365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB

Common Values

ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Length

2023-10-02T15:01:30.442141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:30.490947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 12020
30.3%
a 10085
25.4%
c 8098
20.4%
d 5307
13.4%
e 2842
 
7.2%
f 1049
 
2.6%
g 316
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39717
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 39717
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

sub_grade
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B3
2917 
A4
2886 
A5
2742 
B5
2704 
B4
 
2512
Other values (30)
25956 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5

Common Values

ValueCountFrequency (%)
B3 2917
 
7.3%
A4 2886
 
7.3%
A5 2742
 
6.9%
B5 2704
 
6.8%
B4 2512
 
6.3%
C1 2136
 
5.4%
B2 2057
 
5.2%
C2 2011
 
5.1%
B1 1830
 
4.6%
A3 1810
 
4.6%
Other values (25) 16112
40.6%

Length

2023-10-02T15:01:30.546138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 2917
 
7.3%
a4 2886
 
7.3%
a5 2742
 
6.9%
b5 2704
 
6.8%
b4 2512
 
6.3%
c1 2136
 
5.4%
b2 2057
 
5.2%
c2 2011
 
5.1%
b1 1830
 
4.6%
a3 1810
 
4.6%
Other values (25) 16112
40.6%

Most occurring characters

ValueCountFrequency (%)
B 12020
15.1%
A 10085
12.7%
4 8293
10.4%
3 8215
10.3%
C 8098
10.2%
5 8070
10.2%
2 7907
10.0%
1 7232
9.1%
D 5307
6.7%
E 2842
 
3.6%
Other values (2) 1365
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39717
50.0%
Decimal Number 39717
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 8293
20.9%
3 8215
20.7%
5 8070
20.3%
2 7907
19.9%
1 7232
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 39717
50.0%
Common 39717
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%
Common
ValueCountFrequency (%)
4 8293
20.9%
3 8215
20.7%
5 8070
20.3%
2 7907
19.9%
1 7232
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 12020
15.1%
A 10085
12.7%
4 8293
10.4%
3 8215
10.3%
C 8098
10.2%
5 8070
10.2%
2 7907
10.0%
1 7232
9.1%
D 5307
6.7%
E 2842
 
3.6%
Other values (2) 1365
 
1.7%

emp_title
Text

MISSING 

Distinct28820
Distinct (%)77.4%
Missing2459
Missing (%)6.2%
Memory size310.4 KiB
2023-10-02T15:01:30.738879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length55
Mean length18.379784
Min length2

Characters and Unicode

Total characters684794
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25641 ?
Unique (%)68.8%

Sample

1st rowRyder
2nd rowAIR RESOURCES BOARD
3rd rowUniversity Medical Group
4th rowVeolia Transportaton
5th rowSouthern Star Photography
ValueCountFrequency (%)
inc 3197
 
3.2%
of 3008
 
3.0%
1208
 
1.2%
and 963
 
1.0%
center 818
 
0.8%
bank 805
 
0.8%
county 803
 
0.8%
services 795
 
0.8%
school 750
 
0.7%
the 747
 
0.7%
Other values (18882) 87491
87.0%
2023-10-02T15:01:31.028400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64766
 
9.5%
e 55954
 
8.2%
a 43836
 
6.4%
n 42641
 
6.2%
o 42586
 
6.2%
i 40491
 
5.9%
r 40067
 
5.9%
t 38580
 
5.6%
s 30254
 
4.4%
l 25923
 
3.8%
Other values (86) 259696
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 489338
71.5%
Uppercase Letter 119545
 
17.5%
Space Separator 64766
 
9.5%
Other Punctuation 8798
 
1.3%
Dash Punctuation 1031
 
0.2%
Decimal Number 968
 
0.1%
Open Punctuation 159
 
< 0.1%
Close Punctuation 156
 
< 0.1%
Math Symbol 21
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 14579
 
12.2%
S 13325
 
11.1%
A 8885
 
7.4%
I 7566
 
6.3%
M 6518
 
5.5%
P 6077
 
5.1%
T 5691
 
4.8%
L 5561
 
4.7%
E 5241
 
4.4%
D 5056
 
4.2%
Other values (18) 41046
34.3%
Lowercase Letter
ValueCountFrequency (%)
e 55954
11.4%
a 43836
9.0%
n 42641
8.7%
o 42586
8.7%
i 40491
 
8.3%
r 40067
 
8.2%
t 38580
 
7.9%
s 30254
 
6.2%
l 25923
 
5.3%
c 23099
 
4.7%
Other values (17) 105907
21.6%
Other Punctuation
ValueCountFrequency (%)
. 4253
48.3%
, 2194
24.9%
& 1301
 
14.8%
' 652
 
7.4%
/ 311
 
3.5%
# 36
 
0.4%
@ 10
 
0.1%
: 9
 
0.1%
" 8
 
0.1%
! 8
 
0.1%
Other values (5) 16
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 192
19.8%
2 161
16.6%
3 155
16.0%
0 98
10.1%
4 91
9.4%
5 72
 
7.4%
9 62
 
6.4%
6 58
 
6.0%
7 46
 
4.8%
8 33
 
3.4%
Math Symbol
ValueCountFrequency (%)
+ 18
85.7%
| 2
 
9.5%
< 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 158
99.4%
[ 1
 
0.6%
Currency Symbol
ValueCountFrequency (%)
¢ 1
50.0%
$ 1
50.0%
Control
ValueCountFrequency (%)
€ 1
50.0%
ƒ 1
50.0%
Space Separator
ValueCountFrequency (%)
64766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1031
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 2
100.0%
Other Number
ValueCountFrequency (%)
² 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 608883
88.9%
Common 75911
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 55954
 
9.2%
a 43836
 
7.2%
n 42641
 
7.0%
o 42586
 
7.0%
i 40491
 
6.7%
r 40067
 
6.6%
t 38580
 
6.3%
s 30254
 
5.0%
l 25923
 
4.3%
c 23099
 
3.8%
Other values (45) 225452
37.0%
Common
ValueCountFrequency (%)
64766
85.3%
. 4253
 
5.6%
, 2194
 
2.9%
& 1301
 
1.7%
- 1031
 
1.4%
' 652
 
0.9%
/ 311
 
0.4%
1 192
 
0.3%
2 161
 
0.2%
( 158
 
0.2%
Other values (31) 892
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684780
> 99.9%
None 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64766
 
9.5%
e 55954
 
8.2%
a 43836
 
6.4%
n 42641
 
6.2%
o 42586
 
6.2%
i 40491
 
5.9%
r 40067
 
5.9%
t 38580
 
5.6%
s 30254
 
4.4%
l 25923
 
3.8%
Other values (77) 259682
37.9%
None
ValueCountFrequency (%)
à 3
21.4%
© 2
14.3%
² 2
14.3%
 2
14.3%
¢ 1
 
7.1%
€ 1
 
7.1%
â 1
 
7.1%
ƒ 1
 
7.1%
¡ 1
 
7.1%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing1075
Missing (%)2.7%
Memory size310.4 KiB
10+ years
8879 
< 1 year
4583 
2 years
4388 
3 years
4095 
4 years
3436 
Other values (6)
13261 

Length

Max length9
Median length7
Mean length7.4943067
Min length6

Characters and Unicode

Total characters289595
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year

Common Values

ValueCountFrequency (%)
10+ years 8879
22.4%
< 1 year 4583
11.5%
2 years 4388
11.0%
3 years 4095
10.3%
4 years 3436
 
8.7%
5 years 3282
 
8.3%
1 year 3240
 
8.2%
6 years 2229
 
5.6%
7 years 1773
 
4.5%
8 years 1479
 
3.7%

Length

2023-10-02T15:01:31.114477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 30819
37.6%
10 8879
 
10.8%
1 7823
 
9.6%
year 7823
 
9.6%
4583
 
5.6%
2 4388
 
5.4%
3 4095
 
5.0%
4 3436
 
4.2%
5 3282
 
4.0%
6 2229
 
2.7%
Other values (3) 4510
 
5.5%

Most occurring characters

ValueCountFrequency (%)
43225
14.9%
y 38642
13.3%
e 38642
13.3%
a 38642
13.3%
r 38642
13.3%
s 30819
10.6%
1 16702
 
5.8%
0 8879
 
3.1%
+ 8879
 
3.1%
< 4583
 
1.6%
Other values (8) 21940
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185387
64.0%
Decimal Number 47521
 
16.4%
Space Separator 43225
 
14.9%
Math Symbol 13462
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16702
35.1%
0 8879
18.7%
2 4388
 
9.2%
3 4095
 
8.6%
4 3436
 
7.2%
5 3282
 
6.9%
6 2229
 
4.7%
7 1773
 
3.7%
8 1479
 
3.1%
9 1258
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
y 38642
20.8%
e 38642
20.8%
a 38642
20.8%
r 38642
20.8%
s 30819
16.6%
Math Symbol
ValueCountFrequency (%)
+ 8879
66.0%
< 4583
34.0%
Space Separator
ValueCountFrequency (%)
43225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185387
64.0%
Common 104208
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
43225
41.5%
1 16702
 
16.0%
0 8879
 
8.5%
+ 8879
 
8.5%
< 4583
 
4.4%
2 4388
 
4.2%
3 4095
 
3.9%
4 3436
 
3.3%
5 3282
 
3.1%
6 2229
 
2.1%
Other values (3) 4510
 
4.3%
Latin
ValueCountFrequency (%)
y 38642
20.8%
e 38642
20.8%
a 38642
20.8%
r 38642
20.8%
s 30819
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43225
14.9%
y 38642
13.3%
e 38642
13.3%
a 38642
13.3%
r 38642
13.3%
s 30819
10.6%
1 16702
 
5.8%
0 8879
 
3.1%
+ 8879
 
3.1%
< 4583
 
1.6%
Other values (8) 21940
7.6%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
RENT
18899 
MORTGAGE
17659 
OWN
3058 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length5
Mean length5.7039555
Min length3

Characters and Unicode

Total characters226544
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 18899
47.6%
MORTGAGE 17659
44.5%
OWN 3058
 
7.7%
OTHER 98
 
0.2%
NONE 3
 
< 0.1%

Length

2023-10-02T15:01:31.166325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:31.216620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rent 18899
47.6%
mortgage 17659
44.5%
own 3058
 
7.7%
other 98
 
0.2%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 226544
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 226544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

annual_inc
Real number (ℝ)

SKEWED 

Distinct5318
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68968.926
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:31.274294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140404
median59000
Q382300
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41896

Descriptive statistics

Standard deviation63793.766
Coefficient of variation (CV)0.92496388
Kurtosis2302.7378
Mean68968.926
Median Absolute Deviation (MAD)20000
Skewness30.949185
Sum2.7392388 × 109
Variance4.0696446 × 109
MonotonicityNot monotonic
2023-10-02T15:01:31.336835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1505
 
3.8%
50000 1057
 
2.7%
40000 876
 
2.2%
45000 830
 
2.1%
30000 825
 
2.1%
75000 811
 
2.0%
65000 803
 
2.0%
70000 733
 
1.8%
48000 723
 
1.8%
80000 662
 
1.7%
Other values (5308) 30892
77.8%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4080 1
 
< 0.1%
4200 2
 
< 0.1%
4800 4
< 0.1%
4888 1
 
< 0.1%
5000 1
 
< 0.1%
5500 1
 
< 0.1%
6000 5
< 0.1%
7000 1
 
< 0.1%
7200 4
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 1
 
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 4
< 0.1%
1176000 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Not Verified
16921 
Verified
12809 
Source Verified
9987 

Length

Max length15
Median length12
Mean length11.464335
Min length8

Characters and Unicode

Total characters455329
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Not Verified 16921
42.6%
Verified 12809
32.3%
Source Verified 9987
25.1%

Length

2023-10-02T15:01:31.399572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:31.452195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 39717
59.6%
not 16921
25.4%
source 9987
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e 89421
19.6%
i 79434
17.4%
r 49704
10.9%
V 39717
8.7%
f 39717
8.7%
d 39717
8.7%
o 26908
 
5.9%
26908
 
5.9%
N 16921
 
3.7%
t 16921
 
3.7%
Other values (3) 29961
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 361796
79.5%
Uppercase Letter 66625
 
14.6%
Space Separator 26908
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 89421
24.7%
i 79434
22.0%
r 49704
13.7%
f 39717
11.0%
d 39717
11.0%
o 26908
 
7.4%
t 16921
 
4.7%
u 9987
 
2.8%
c 9987
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 39717
59.6%
N 16921
25.4%
S 9987
 
15.0%
Space Separator
ValueCountFrequency (%)
26908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428421
94.1%
Common 26908
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 89421
20.9%
i 79434
18.5%
r 49704
11.6%
V 39717
9.3%
f 39717
9.3%
d 39717
9.3%
o 26908
 
6.3%
N 16921
 
3.9%
t 16921
 
3.9%
S 9987
 
2.3%
Other values (2) 19974
 
4.7%
Common
ValueCountFrequency (%)
26908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 89421
19.6%
i 79434
17.4%
r 49704
10.9%
V 39717
8.7%
f 39717
8.7%
d 39717
8.7%
o 26908
 
5.9%
26908
 
5.9%
N 16921
 
3.7%
t 16921
 
3.7%
Other values (3) 29961
 
6.6%
Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:31.578245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11
ValueCountFrequency (%)
dec-11 2260
 
5.7%
nov-11 2223
 
5.6%
oct-11 2114
 
5.3%
sep-11 2063
 
5.2%
aug-11 1928
 
4.9%
jul-11 1870
 
4.7%
jun-11 1827
 
4.6%
may-11 1689
 
4.3%
apr-11 1562
 
3.9%
mar-11 1443
 
3.6%
Other values (45) 20738
52.2%
2023-10-02T15:01:31.778946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 54844
23.0%
- 39717
16.7%
0 18061
 
7.6%
e 10439
 
4.4%
u 10273
 
4.3%
J 9134
 
3.8%
c 8367
 
3.5%
a 8070
 
3.4%
p 6482
 
2.7%
A 6352
 
2.7%
Other values (18) 66563
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79434
33.3%
Lowercase Letter 79434
33.3%
Dash Punctuation 39717
16.7%
Uppercase Letter 39717
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10439
13.1%
u 10273
12.9%
c 8367
10.5%
a 8070
10.2%
p 6482
8.2%
n 5658
7.1%
r 5526
7.0%
v 4167
 
5.2%
o 4167
 
5.2%
t 3934
 
5.0%
Other values (4) 12351
15.5%
Uppercase Letter
ValueCountFrequency (%)
J 9134
23.0%
A 6352
16.0%
M 5691
14.3%
D 4433
11.2%
N 4167
10.5%
O 3934
9.9%
S 3648
 
9.2%
F 2358
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 54844
69.0%
0 18061
 
22.7%
9 4716
 
5.9%
8 1562
 
2.0%
7 251
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119151
50.0%
Latin 119151
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10439
 
8.8%
u 10273
 
8.6%
J 9134
 
7.7%
c 8367
 
7.0%
a 8070
 
6.8%
p 6482
 
5.4%
A 6352
 
5.3%
M 5691
 
4.8%
n 5658
 
4.7%
r 5526
 
4.6%
Other values (12) 43159
36.2%
Common
ValueCountFrequency (%)
1 54844
46.0%
- 39717
33.3%
0 18061
 
15.2%
9 4716
 
4.0%
8 1562
 
1.3%
7 251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54844
23.0%
- 39717
16.7%
0 18061
 
7.6%
e 10439
 
4.4%
u 10273
 
4.3%
J 9134
 
3.8%
c 8367
 
3.5%
a 8070
 
3.4%
p 6482
 
2.7%
A 6352
 
2.7%
Other values (18) 66563
27.9%

loan_status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Fully Paid
32950 
Charged Off
5627 
Current
 
1140

Length

Max length11
Median length10
Mean length10.055568
Min length7

Characters and Unicode

Total characters399377
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowCurrent

Common Values

ValueCountFrequency (%)
Fully Paid 32950
83.0%
Charged Off 5627
 
14.2%
Current 1140
 
2.9%

Length

2023-10-02T15:01:31.857593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:31.906493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 32950
42.1%
paid 32950
42.1%
charged 5627
 
7.2%
off 5627
 
7.2%
current 1140
 
1.5%

Most occurring characters

ValueCountFrequency (%)
l 65900
16.5%
38577
9.7%
a 38577
9.7%
d 38577
9.7%
u 34090
8.5%
F 32950
8.3%
y 32950
8.3%
P 32950
8.3%
i 32950
8.3%
f 11254
 
2.8%
Other values (8) 40602
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 282506
70.7%
Uppercase Letter 78294
 
19.6%
Space Separator 38577
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 65900
23.3%
a 38577
13.7%
d 38577
13.7%
u 34090
12.1%
y 32950
11.7%
i 32950
11.7%
f 11254
 
4.0%
r 7907
 
2.8%
e 6767
 
2.4%
g 5627
 
2.0%
Other values (3) 7907
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
F 32950
42.1%
P 32950
42.1%
C 6767
 
8.6%
O 5627
 
7.2%
Space Separator
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360800
90.3%
Common 38577
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 65900
18.3%
a 38577
10.7%
d 38577
10.7%
u 34090
9.4%
F 32950
9.1%
y 32950
9.1%
P 32950
9.1%
i 32950
9.1%
f 11254
 
3.1%
r 7907
 
2.2%
Other values (7) 32695
9.1%
Common
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 65900
16.5%
38577
9.7%
a 38577
9.7%
d 38577
9.7%
u 34090
8.5%
F 32950
8.3%
y 32950
8.3%
P 32950
8.3%
i 32950
8.3%
f 11254
 
2.8%
Other values (8) 40602
10.2%

pymnt_plan
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False 39717
100.0%
2023-10-02T15:01:31.949290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

url
Text

UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:32.259929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length63
Mean length63.108367
Min length62

Characters and Unicode

Total characters2506475
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39717 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=1077501 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069639 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069742 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1062474 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069971 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1077175 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1076863 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1075358 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1075269 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1072053 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
2023-10-02T15:01:32.644204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 198585
 
7.9%
l 198585
 
7.9%
n 198585
 
7.9%
a 158868
 
6.3%
t 158868
 
6.3%
/ 158868
 
6.3%
i 158868
 
6.3%
c 119151
 
4.8%
e 119151
 
4.8%
. 79434
 
3.2%
Other values (25) 957512
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1826982
72.9%
Other Punctuation 317736
 
12.7%
Decimal Number 242606
 
9.7%
Uppercase Letter 39717
 
1.6%
Connector Punctuation 39717
 
1.6%
Math Symbol 39717
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 198585
10.9%
l 198585
10.9%
n 198585
10.9%
a 158868
8.7%
t 158868
8.7%
i 158868
8.7%
c 119151
 
6.5%
e 119151
 
6.5%
b 79434
 
4.3%
d 79434
 
4.3%
Other values (8) 357453
19.6%
Decimal Number
ValueCountFrequency (%)
5 26616
11.0%
6 26607
11.0%
7 26037
10.7%
8 25774
10.6%
4 25584
10.5%
1 24160
10.0%
0 23856
9.8%
3 22052
9.1%
9 21694
8.9%
2 20226
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 158868
50.0%
. 79434
25.0%
? 39717
 
12.5%
: 39717
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 39717
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 39717
100.0%
Math Symbol
ValueCountFrequency (%)
= 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1866699
74.5%
Common 639776
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 198585
10.6%
l 198585
10.6%
n 198585
10.6%
a 158868
 
8.5%
t 158868
 
8.5%
i 158868
 
8.5%
c 119151
 
6.4%
e 119151
 
6.4%
b 79434
 
4.3%
d 79434
 
4.3%
Other values (9) 397170
21.3%
Common
ValueCountFrequency (%)
/ 158868
24.8%
. 79434
12.4%
? 39717
 
6.2%
_ 39717
 
6.2%
= 39717
 
6.2%
: 39717
 
6.2%
5 26616
 
4.2%
6 26607
 
4.2%
7 26037
 
4.1%
8 25774
 
4.0%
Other values (6) 137572
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2506475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 198585
 
7.9%
l 198585
 
7.9%
n 198585
 
7.9%
a 158868
 
6.3%
t 158868
 
6.3%
/ 158868
 
6.3%
i 158868
 
6.3%
c 119151
 
4.8%
e 119151
 
4.8%
. 79434
 
3.2%
Other values (25) 957512
38.2%

desc
Text

MISSING 

Distinct26527
Distinct (%)99.1%
Missing12940
Missing (%)32.6%
Memory size310.4 KiB
2023-10-02T15:01:33.018119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3988
Median length2248
Mean length426.49404
Min length1

Characters and Unicode

Total characters11420231
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26499 ?
Unique (%)99.0%

Sample

1st row Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>
2nd row Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>
3rd row Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>
4th row Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>
5th row Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>
ValueCountFrequency (%)
i 77512
 
3.8%
to 71096
 
3.5%
a 54855
 
2.7%
the 54340
 
2.7%
and 54329
 
2.6%
my 51308
 
2.5%
on 49132
 
2.4%
37238
 
1.8%
for 32774
 
1.6%
have 32490
 
1.6%
Other values (53986) 1535186
74.9%
2023-10-02T15:01:33.380083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2121185
18.6%
e 953964
 
8.4%
a 714029
 
6.3%
o 709013
 
6.2%
t 649103
 
5.7%
n 612137
 
5.4%
r 589058
 
5.2%
i 496003
 
4.3%
s 426272
 
3.7%
d 397984
 
3.5%
Other values (132) 3751483
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8135704
71.2%
Space Separator 2121258
 
18.6%
Decimal Number 346663
 
3.0%
Other Punctuation 326744
 
2.9%
Uppercase Letter 302772
 
2.7%
Math Symbol 140645
 
1.2%
Currency Symbol 16745
 
0.1%
Dash Punctuation 13032
 
0.1%
Close Punctuation 7337
 
0.1%
Open Punctuation 6727
 
0.1%
Other values (7) 2604
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 94559
31.2%
B 34778
 
11.5%
T 28474
 
9.4%
A 15522
 
5.1%
C 14303
 
4.7%
M 14262
 
4.7%
S 9642
 
3.2%
E 9211
 
3.0%
W 8830
 
2.9%
L 8656
 
2.9%
Other values (21) 64535
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 953964
11.7%
a 714029
 
8.8%
o 709013
 
8.7%
t 649103
 
8.0%
n 612137
 
7.5%
r 589058
 
7.2%
i 496003
 
6.1%
s 426272
 
5.2%
d 397984
 
4.9%
l 355636
 
4.4%
Other values (18) 2232505
27.4%
Other Punctuation
ValueCountFrequency (%)
. 120658
36.9%
/ 116441
35.6%
, 50144
15.3%
' 13317
 
4.1%
! 6738
 
2.1%
% 5704
 
1.7%
: 5281
 
1.6%
; 3357
 
1.0%
& 2616
 
0.8%
" 801
 
0.2%
Other values (10) 1687
 
0.5%
Control
ValueCountFrequency (%)
1287
60.5%
€ 411
 
19.3%
™ 191
 
9.0%
’ 38
 
1.8%
“ 37
 
1.7%
‚ 35
 
1.6%
 27
 
1.3%
ƒ 27
 
1.3%
œ 23
 
1.1%
š 15
 
0.7%
Other values (9) 37
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 104184
30.1%
1 97487
28.1%
2 36710
 
10.6%
5 21465
 
6.2%
3 17828
 
5.1%
9 16291
 
4.7%
4 13709
 
4.0%
6 13182
 
3.8%
7 12927
 
3.7%
8 12880
 
3.7%
Math Symbol
ValueCountFrequency (%)
> 84845
60.3%
< 53870
38.3%
+ 984
 
0.7%
= 615
 
0.4%
~ 290
 
0.2%
¬ 31
 
< 0.1%
| 10
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
¦ 96
83.5%
© 15
 
13.0%
2
 
1.7%
® 2
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 13017
99.9%
9
 
0.1%
6
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 7290
99.4%
] 44
 
0.6%
} 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6681
99.3%
[ 44
 
0.7%
{ 2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 13
68.4%
^ 5
 
26.3%
¯ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
2121185
> 99.9%
  73
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 16671
99.6%
¢ 74
 
0.4%
Final Punctuation
ValueCountFrequency (%)
78
81.2%
18
 
18.8%
Initial Punctuation
ValueCountFrequency (%)
18
85.7%
3
 
14.3%
Other Number
ValueCountFrequency (%)
½ 6
75.0%
¾ 2
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8438476
73.9%
Common 2981755
 
26.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2121185
71.1%
. 120658
 
4.0%
/ 116441
 
3.9%
0 104184
 
3.5%
1 97487
 
3.3%
> 84845
 
2.8%
< 53870
 
1.8%
, 50144
 
1.7%
2 36710
 
1.2%
5 21465
 
0.7%
Other values (73) 174766
 
5.9%
Latin
ValueCountFrequency (%)
e 953964
 
11.3%
a 714029
 
8.5%
o 709013
 
8.4%
t 649103
 
7.7%
n 612137
 
7.3%
r 589058
 
7.0%
i 496003
 
5.9%
s 426272
 
5.1%
d 397984
 
4.7%
l 355636
 
4.2%
Other values (49) 2535277
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11418236
> 99.9%
None 1834
 
< 0.1%
Punctuation 159
 
< 0.1%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2121185
18.6%
e 953964
 
8.4%
a 714029
 
6.3%
o 709013
 
6.2%
t 649103
 
5.7%
n 612137
 
5.4%
r 589058
 
5.2%
i 496003
 
4.3%
s 426272
 
3.7%
d 397984
 
3.5%
Other values (86) 3749488
32.8%
None
ValueCountFrequency (%)
â 438
23.9%
€ 411
22.4%
™ 191
10.4%
 127
 
6.9%
à 97
 
5.3%
¦ 96
 
5.2%
¢ 74
 
4.0%
  73
 
4.0%
’ 38
 
2.1%
“ 37
 
2.0%
Other values (27) 252
13.7%
Punctuation
ValueCountFrequency (%)
78
49.1%
19
 
11.9%
18
 
11.3%
18
 
11.3%
9
 
5.7%
8
 
5.0%
6
 
3.8%
3
 
1.9%
Specials
ValueCountFrequency (%)
2
100.0%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
debt_consolidation
18641 
credit_card
5130 
other
3993 
home_improvement
2976 
major_purchase
2187 
Other values (9)
6790 

Length

Max length18
Median length16
Mean length13.736183
Min length3

Characters and Unicode

Total characters545560
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation 18641
46.9%
credit_card 5130
 
12.9%
other 3993
 
10.1%
home_improvement 2976
 
7.5%
major_purchase 2187
 
5.5%
small_business 1828
 
4.6%
car 1549
 
3.9%
wedding 947
 
2.4%
medical 693
 
1.7%
moving 583
 
1.5%
Other values (4) 1190
 
3.0%

Length

2023-10-02T15:01:33.466776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 18641
46.9%
credit_card 5130
 
12.9%
other 3993
 
10.1%
home_improvement 2976
 
7.5%
major_purchase 2187
 
5.5%
small_business 1828
 
4.6%
car 1549
 
3.9%
wedding 947
 
2.4%
medical 693
 
1.7%
moving 583
 
1.5%
Other values (4) 1190
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 69725
12.8%
d 50454
9.2%
i 50145
9.2%
t 50087
9.2%
n 44528
8.2%
e 43568
 
8.0%
c 34036
 
6.2%
a 33730
 
6.2%
_ 30865
 
5.7%
s 28521
 
5.2%
Other values (12) 109901
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 514695
94.3%
Connector Punctuation 30865
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 69725
13.5%
d 50454
9.8%
i 50145
9.7%
t 50087
9.7%
n 44528
8.7%
e 43568
8.5%
c 34036
 
6.6%
a 33730
 
6.6%
s 28521
 
5.5%
l 23418
 
4.5%
Other values (11) 86483
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 30865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 514695
94.3%
Common 30865
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 69725
13.5%
d 50454
9.8%
i 50145
9.7%
t 50087
9.7%
n 44528
8.7%
e 43568
8.5%
c 34036
 
6.6%
a 33730
 
6.6%
s 28521
 
5.5%
l 23418
 
4.5%
Other values (11) 86483
16.8%
Common
ValueCountFrequency (%)
_ 30865
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 545560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 69725
12.8%
d 50454
9.2%
i 50145
9.2%
t 50087
9.2%
n 44528
8.2%
e 43568
 
8.0%
c 34036
 
6.2%
a 33730
 
6.2%
_ 30865
 
5.7%
s 28521
 
5.2%
Other values (12) 109901
20.1%

title
Text

Distinct19615
Distinct (%)49.4%
Missing11
Missing (%)< 0.1%
Memory size310.4 KiB
2023-10-02T15:01:33.660990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length72
Mean length17.187327
Min length1

Characters and Unicode

Total characters682440
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17624 ?
Unique (%)44.4%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowPersonal
ValueCountFrequency (%)
loan 10895
 
10.4%
debt 9245
 
8.8%
consolidation 8622
 
8.2%
credit 4604
 
4.4%
card 3341
 
3.2%
personal 2043
 
2.0%
home 1875
 
1.8%
pay 1344
 
1.3%
off 1259
 
1.2%
my 1133
 
1.1%
Other values (8935) 60203
57.6%
2023-10-02T15:01:33.947775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66029
 
9.7%
o 65729
 
9.6%
n 55657
 
8.2%
e 54557
 
8.0%
a 50167
 
7.4%
i 43822
 
6.4%
t 42600
 
6.2%
d 30679
 
4.5%
r 29153
 
4.3%
s 28544
 
4.2%
Other values (98) 215503
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521300
76.4%
Uppercase Letter 83242
 
12.2%
Space Separator 66029
 
9.7%
Decimal Number 5995
 
0.9%
Other Punctuation 4442
 
0.7%
Dash Punctuation 824
 
0.1%
Connector Punctuation 213
 
< 0.1%
Close Punctuation 104
 
< 0.1%
Currency Symbol 94
 
< 0.1%
Math Symbol 92
 
< 0.1%
Other values (5) 105
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 65729
12.6%
n 55657
10.7%
e 54557
10.5%
a 50167
9.6%
i 43822
8.4%
t 42600
8.2%
d 30679
 
5.9%
r 29153
 
5.6%
s 28544
 
5.5%
l 26300
 
5.0%
Other values (18) 94092
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 18509
22.2%
L 10335
12.4%
D 9244
11.1%
P 5641
 
6.8%
R 3732
 
4.5%
M 3256
 
3.9%
S 3227
 
3.9%
B 3116
 
3.7%
H 2910
 
3.5%
I 2885
 
3.5%
Other values (18) 20387
24.5%
Other Punctuation
ValueCountFrequency (%)
! 1123
25.3%
' 982
22.1%
. 712
16.0%
/ 538
12.1%
, 435
 
9.8%
& 328
 
7.4%
% 95
 
2.1%
: 64
 
1.4%
" 56
 
1.3%
? 25
 
0.6%
Other values (5) 84
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 1691
28.2%
0 1677
28.0%
2 1105
18.4%
3 299
 
5.0%
5 256
 
4.3%
9 254
 
4.2%
4 216
 
3.6%
6 178
 
3.0%
8 169
 
2.8%
7 150
 
2.5%
Control
ValueCountFrequency (%)
€ 4
21.1%
— 4
21.1%
 4
21.1%
2
10.5%
™ 2
10.5%
‚ 1
 
5.3%
… 1
 
5.3%
– 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
+ 53
57.6%
= 19
 
20.7%
< 9
 
9.8%
> 8
 
8.7%
~ 2
 
2.2%
| 1
 
1.1%
Modifier Symbol
ValueCountFrequency (%)
^ 1
33.3%
´ 1
33.3%
` 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 100
96.2%
] 4
 
3.8%
Open Punctuation
ValueCountFrequency (%)
( 77
96.2%
[ 3
 
3.8%
Space Separator
ValueCountFrequency (%)
66029
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 824
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 213
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 94
100.0%
Other Symbol
ValueCountFrequency (%)
¦ 2
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 604542
88.6%
Common 77898
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 65729
 
10.9%
n 55657
 
9.2%
e 54557
 
9.0%
a 50167
 
8.3%
i 43822
 
7.2%
t 42600
 
7.0%
d 30679
 
5.1%
r 29153
 
4.8%
s 28544
 
4.7%
l 26300
 
4.4%
Other values (46) 177334
29.3%
Common
ValueCountFrequency (%)
66029
84.8%
1 1691
 
2.2%
0 1677
 
2.2%
! 1123
 
1.4%
2 1105
 
1.4%
' 982
 
1.3%
- 824
 
1.1%
. 712
 
0.9%
/ 538
 
0.7%
, 435
 
0.6%
Other values (42) 2782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 682408
> 99.9%
None 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66029
 
9.7%
o 65729
 
9.6%
n 55657
 
8.2%
e 54557
 
8.0%
a 50167
 
7.4%
i 43822
 
6.4%
t 42600
 
6.2%
d 30679
 
4.5%
r 29153
 
4.3%
s 28544
 
4.2%
Other values (84) 215471
31.6%
None
ValueCountFrequency (%)
â 4
12.5%
€ 4
12.5%
— 4
12.5%
 4
12.5%
î 4
12.5%
¦ 2
6.2%
™ 2
6.2%
à 2
6.2%
´ 1
 
3.1%
 1
 
3.1%
Other values (4) 4
12.5%
Distinct823
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:34.216753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters198585
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx
ValueCountFrequency (%)
100xx 597
 
1.5%
945xx 545
 
1.4%
112xx 516
 
1.3%
606xx 503
 
1.3%
070xx 473
 
1.2%
900xx 453
 
1.1%
021xx 397
 
1.0%
300xx 394
 
1.0%
926xx 371
 
0.9%
750xx 367
 
0.9%
Other values (813) 35101
88.4%
2023-10-02T15:01:34.538480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 79434
40.0%
0 19773
 
10.0%
1 15629
 
7.9%
2 13589
 
6.8%
9 12681
 
6.4%
3 12356
 
6.2%
7 10257
 
5.2%
4 9121
 
4.6%
5 9020
 
4.5%
8 8670
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119151
60.0%
Lowercase Letter 79434
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19773
16.6%
1 15629
13.1%
2 13589
11.4%
9 12681
10.6%
3 12356
10.4%
7 10257
8.6%
4 9121
7.7%
5 9020
7.6%
8 8670
7.3%
6 8055
6.8%
Lowercase Letter
ValueCountFrequency (%)
x 79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119151
60.0%
Latin 79434
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19773
16.6%
1 15629
13.1%
2 13589
11.4%
9 12681
10.6%
3 12356
10.4%
7 10257
8.6%
4 9121
7.7%
5 9020
7.6%
8 8670
7.3%
6 8055
6.8%
Latin
ValueCountFrequency (%)
x 79434
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 79434
40.0%
0 19773
 
10.0%
1 15629
 
7.9%
2 13589
 
6.8%
9 12681
 
6.4%
3 12356
 
6.2%
7 10257
 
5.2%
4 9121
 
4.6%
5 9020
 
4.5%
8 8670
 
4.4%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
CA
7099 
NY
3812 
FL
2866 
TX
2727 
NJ
 
1850
Other values (45)
21363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR

Common Values

ValueCountFrequency (%)
CA 7099
17.9%
NY 3812
 
9.6%
FL 2866
 
7.2%
TX 2727
 
6.9%
NJ 1850
 
4.7%
IL 1525
 
3.8%
PA 1517
 
3.8%
VA 1407
 
3.5%
GA 1398
 
3.5%
MA 1340
 
3.4%
Other values (40) 14176
35.7%

Length

2023-10-02T15:01:34.621616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 7099
17.9%
ny 3812
 
9.6%
fl 2866
 
7.2%
tx 2727
 
6.9%
nj 1850
 
4.7%
il 1525
 
3.8%
pa 1517
 
3.8%
va 1407
 
3.5%
ga 1398
 
3.5%
ma 1340
 
3.4%
Other values (40) 14176
35.7%

Most occurring characters

ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79434
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 79434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

dti
Real number (ℝ)

Distinct2868
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31513
Minimum0
Maximum29.99
Zeros183
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:34.673895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.17
median13.4
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.6785936
Coefficient of variation (CV)0.50157932
Kurtosis-0.85201548
Mean13.31513
Median Absolute Deviation (MAD)5.21
Skewness-0.028043331
Sum528837
Variance44.603612
MonotonicityNot monotonic
2023-10-02T15:01:34.734359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
0.5%
12 51
 
0.1%
18 45
 
0.1%
19.2 40
 
0.1%
13.2 39
 
0.1%
12.48 38
 
0.1%
16.8 38
 
0.1%
13.5 38
 
0.1%
6 37
 
0.1%
15 36
 
0.1%
Other values (2858) 39172
98.6%
ValueCountFrequency (%)
0 183
0.5%
0.01 3
 
< 0.1%
0.02 5
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
< 0.1%
0.08 5
 
< 0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
29.99 1
 
< 0.1%
29.95 1
 
< 0.1%
29.93 3
< 0.1%
29.92 2
< 0.1%
29.89 1
 
< 0.1%
29.88 1
 
< 0.1%
29.86 2
< 0.1%
29.85 1
 
< 0.1%
29.83 1
 
< 0.1%
29.82 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14651157
Minimum0
Maximum11
Zeros35405
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:34.786174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49181152
Coefficient of variation (CV)3.3568101
Kurtosis39.4125
Mean0.14651157
Median Absolute Deviation (MAD)0
Skewness5.0220352
Sum5819
Variance0.24187857
MonotonicityNot monotonic
2023-10-02T15:01:34.831915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 35405
89.1%
1 3303
 
8.3%
2 687
 
1.7%
3 220
 
0.6%
4 62
 
0.2%
5 22
 
0.1%
6 10
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 35405
89.1%
1 3303
 
8.3%
2 687
 
1.7%
3 220
 
0.6%
4 62
 
0.2%
5 22
 
0.1%
6 10
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 10
 
< 0.1%
5 22
 
0.1%
4 62
 
0.2%
3 220
 
0.6%
2 687
 
1.7%
1 3303
8.3%
Distinct526
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:35.065661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238302
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.1%

Sample

1st rowJan-85
2nd rowApr-99
3rd rowNov-01
4th rowFeb-96
5th rowJan-96
ValueCountFrequency (%)
nov-98 370
 
0.9%
oct-99 366
 
0.9%
dec-98 348
 
0.9%
oct-00 346
 
0.9%
dec-97 329
 
0.8%
nov-00 320
 
0.8%
nov-99 319
 
0.8%
sep-00 306
 
0.8%
oct-98 305
 
0.8%
nov-97 298
 
0.8%
Other values (516) 36410
91.7%
2023-10-02T15:01:35.381348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 39717
16.7%
9 23353
 
9.8%
0 19365
 
8.1%
e 10541
 
4.4%
J 9426
 
4.0%
u 9302
 
3.9%
a 9126
 
3.8%
8 8453
 
3.5%
c 8143
 
3.4%
n 6364
 
2.7%
Other values (23) 94512
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79434
33.3%
Lowercase Letter 79434
33.3%
Dash Punctuation 39717
16.7%
Uppercase Letter 39717
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10541
13.3%
u 9302
11.7%
a 9126
11.5%
c 8143
10.3%
n 6364
8.0%
p 6335
8.0%
r 5536
7.0%
t 4076
 
5.1%
o 3930
 
4.9%
v 3930
 
4.9%
Other values (4) 12151
15.3%
Decimal Number
ValueCountFrequency (%)
9 23353
29.4%
0 19365
24.4%
8 8453
 
10.6%
7 4822
 
6.1%
4 4274
 
5.4%
5 4201
 
5.3%
6 4174
 
5.3%
3 3784
 
4.8%
1 3736
 
4.7%
2 3272
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
J 9426
23.7%
A 6047
15.2%
M 5697
14.3%
O 4076
10.3%
D 4067
10.2%
N 3930
9.9%
S 3593
 
9.0%
F 2881
 
7.3%
Dash Punctuation
ValueCountFrequency (%)
- 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119151
50.0%
Latin 119151
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10541
 
8.8%
J 9426
 
7.9%
u 9302
 
7.8%
a 9126
 
7.7%
c 8143
 
6.8%
n 6364
 
5.3%
p 6335
 
5.3%
A 6047
 
5.1%
M 5697
 
4.8%
r 5536
 
4.6%
Other values (12) 42634
35.8%
Common
ValueCountFrequency (%)
- 39717
33.3%
9 23353
19.6%
0 19365
16.3%
8 8453
 
7.1%
7 4822
 
4.0%
4 4274
 
3.6%
5 4201
 
3.5%
6 4174
 
3.5%
3 3784
 
3.2%
1 3736
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 39717
16.7%
9 23353
 
9.8%
0 19365
 
8.1%
e 10541
 
4.4%
J 9426
 
4.0%
u 9302
 
3.9%
a 9126
 
3.8%
8 8453
 
3.5%
c 8143
 
3.4%
n 6364
 
2.7%
Other values (23) 94512
39.7%

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86919959
Minimum0
Maximum8
Zeros19300
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:35.459287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0702193
Coefficient of variation (CV)1.23127
Kurtosis2.5621599
Mean0.86919959
Median Absolute Deviation (MAD)1
Skewness1.3903909
Sum34522
Variance1.1453694
MonotonicityNot monotonic
2023-10-02T15:01:35.506511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 19300
48.6%
1 10971
27.6%
2 5812
 
14.6%
3 3048
 
7.7%
4 326
 
0.8%
5 146
 
0.4%
6 64
 
0.2%
7 35
 
0.1%
8 15
 
< 0.1%
ValueCountFrequency (%)
0 19300
48.6%
1 10971
27.6%
2 5812
 
14.6%
3 3048
 
7.7%
4 326
 
0.8%
5 146
 
0.4%
6 64
 
0.2%
7 35
 
0.1%
8 15
 
< 0.1%
ValueCountFrequency (%)
8 15
 
< 0.1%
7 35
 
0.1%
6 64
 
0.2%
5 146
 
0.4%
4 326
 
0.8%
3 3048
 
7.7%
2 5812
 
14.6%
1 10971
27.6%
0 19300
48.6%

mths_since_last_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)0.7%
Missing25682
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean35.900962
Minimum0
Maximum120
Zeros443
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:35.563531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median34
Q352
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.02006
Coefficient of variation (CV)0.6133557
Kurtosis-0.84257778
Mean35.900962
Median Absolute Deviation (MAD)17
Skewness0.30643687
Sum503870
Variance484.88302
MonotonicityNot monotonic
2023-10-02T15:01:35.627545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 443
 
1.1%
15 252
 
0.6%
30 247
 
0.6%
23 247
 
0.6%
24 241
 
0.6%
19 238
 
0.6%
38 237
 
0.6%
20 233
 
0.6%
18 231
 
0.6%
22 231
 
0.6%
Other values (85) 11435
28.8%
(Missing) 25682
64.7%
ValueCountFrequency (%)
0 443
1.1%
1 30
 
0.1%
2 101
 
0.3%
3 145
 
0.4%
4 153
 
0.4%
5 151
 
0.4%
6 192
0.5%
7 176
 
0.4%
8 168
 
0.4%
9 182
0.5%
ValueCountFrequency (%)
120 1
< 0.1%
115 1
< 0.1%
107 1
< 0.1%
106 1
< 0.1%
103 2
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
89 1
< 0.1%
86 2
< 0.1%

mths_since_last_record
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct111
Distinct (%)4.0%
Missing36931
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean69.698134
Minimum0
Maximum129
Zeros670
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:35.688543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median90
Q3104
95-th percentile115
Maximum129
Range129
Interquartile range (IQR)82

Descriptive statistics

Standard deviation43.822529
Coefficient of variation (CV)0.62874753
Kurtosis-1.1565557
Mean69.698134
Median Absolute Deviation (MAD)20
Skewness-0.71722858
Sum194179
Variance1920.4141
MonotonicityNot monotonic
2023-10-02T15:01:35.751738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 670
 
1.7%
104 61
 
0.2%
89 60
 
0.2%
113 59
 
0.1%
111 57
 
0.1%
94 55
 
0.1%
108 55
 
0.1%
93 54
 
0.1%
87 54
 
0.1%
100 53
 
0.1%
Other values (101) 1608
 
4.0%
(Missing) 36931
93.0%
ValueCountFrequency (%)
0 670
1.7%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
17 3
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
120 1
 
< 0.1%
119 10
 
< 0.1%
118 36
0.1%
117 47
0.1%
116 41
0.1%
115 37
0.1%
114 51
0.1%
113 59
0.1%
112 39
0.1%

open_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2944079
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:35.810440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4002825
Coefficient of variation (CV)0.47343333
Kurtosis1.677572
Mean9.2944079
Median Absolute Deviation (MAD)3
Skewness1.0037619
Sum369146
Variance19.362486
MonotonicityNot monotonic
2023-10-02T15:01:35.863583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7 4018
10.1%
6 3946
9.9%
8 3936
9.9%
9 3718
9.4%
10 3223
 
8.1%
5 3183
 
8.0%
11 2746
 
6.9%
4 2343
 
5.9%
12 2273
 
5.7%
13 1911
 
4.8%
Other values (30) 8420
21.2%
ValueCountFrequency (%)
2 605
 
1.5%
3 1493
 
3.8%
4 2343
5.9%
5 3183
8.0%
6 3946
9.9%
7 4018
10.1%
8 3936
9.9%
9 3718
9.4%
10 3223
8.1%
11 2746
6.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 2
 
< 0.1%
35 4
< 0.1%
34 5
< 0.1%
33 3
< 0.1%
32 4
< 0.1%

pub_rec
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
37601 
1
 
2056
2
 
51
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Length

2023-10-02T15:01:35.913345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:35.957504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39717
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 39717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

revol_bal
Real number (ℝ)

ZEROS 

Distinct21711
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13382.528
Minimum0
Maximum149588
Zeros994
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:36.010302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.8
Q13703
median8850
Q317058
95-th percentile41656.4
Maximum149588
Range149588
Interquartile range (IQR)13355

Descriptive statistics

Standard deviation15885.017
Coefficient of variation (CV)1.1869967
Kurtosis14.896523
Mean13382.528
Median Absolute Deviation (MAD)6027
Skewness3.1908837
Sum5.3151387 × 108
Variance2.5233375 × 108
MonotonicityNot monotonic
2023-10-02T15:01:36.071323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 994
 
2.5%
298 14
 
< 0.1%
255 14
 
< 0.1%
1 12
 
< 0.1%
682 11
 
< 0.1%
52 9
 
< 0.1%
6 9
 
< 0.1%
346 9
 
< 0.1%
39 9
 
< 0.1%
10 9
 
< 0.1%
Other values (21701) 38627
97.3%
ValueCountFrequency (%)
0 994
2.5%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
5 8
 
< 0.1%
6 9
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
149588 1
< 0.1%
149527 1
< 0.1%
149000 1
< 0.1%
148829 1
< 0.1%
148804 1
< 0.1%
147897 1
< 0.1%
147750 1
< 0.1%
147559 1
< 0.1%
147451 1
< 0.1%
147365 1
< 0.1%
Distinct1089
Distinct (%)2.7%
Missing50
Missing (%)0.1%
Memory size310.4 KiB
2023-10-02T15:01:36.308657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.52192
Min length2

Characters and Unicode

Total characters219038
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.2%

Sample

1st row83.70%
2nd row9.40%
3rd row98.50%
4th row21%
5th row53.90%
ValueCountFrequency (%)
0 977
 
2.5%
0.20 63
 
0.2%
63 62
 
0.2%
0.10 58
 
0.1%
66.70 58
 
0.1%
40.70 58
 
0.1%
31.20 57
 
0.1%
46.40 57
 
0.1%
66.60 57
 
0.1%
61 57
 
0.1%
Other values (1079) 38163
96.2%
2023-10-02T15:01:36.635871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39671
18.1%
% 39667
18.1%
. 34841
15.9%
4 12082
 
5.5%
5 12063
 
5.5%
6 11989
 
5.5%
7 11949
 
5.5%
3 11885
 
5.4%
2 11550
 
5.3%
8 11419
 
5.2%
Other values (2) 21922
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144530
66.0%
Other Punctuation 74508
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39671
27.4%
4 12082
 
8.4%
5 12063
 
8.3%
6 11989
 
8.3%
7 11949
 
8.3%
3 11885
 
8.2%
2 11550
 
8.0%
8 11419
 
7.9%
1 11111
 
7.7%
9 10811
 
7.5%
Other Punctuation
ValueCountFrequency (%)
% 39667
53.2%
. 34841
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common 219038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39671
18.1%
% 39667
18.1%
. 34841
15.9%
4 12082
 
5.5%
5 12063
 
5.5%
6 11989
 
5.5%
7 11949
 
5.5%
3 11885
 
5.4%
2 11550
 
5.3%
8 11419
 
5.2%
Other values (2) 21922
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39671
18.1%
% 39667
18.1%
. 34841
15.9%
4 12082
 
5.5%
5 12063
 
5.5%
6 11989
 
5.5%
7 11949
 
5.5%
3 11885
 
5.4%
2 11550
 
5.3%
8 11419
 
5.2%
Other values (2) 21922
10.0%

total_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.088828
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:36.724498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.401709
Coefficient of variation (CV)0.51617534
Kurtosis0.6937402
Mean22.088828
Median Absolute Deviation (MAD)7
Skewness0.82737909
Sum877302
Variance129.99896
MonotonicityNot monotonic
2023-10-02T15:01:36.784266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1471
 
3.7%
15 1462
 
3.7%
17 1457
 
3.7%
14 1445
 
3.6%
20 1428
 
3.6%
18 1422
 
3.6%
21 1412
 
3.6%
13 1385
 
3.5%
19 1341
 
3.4%
12 1325
 
3.3%
Other values (72) 25569
64.4%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 182
 
0.5%
4 420
 
1.1%
5 552
1.4%
6 683
1.7%
7 828
2.1%
8 1006
2.5%
9 1080
2.7%
10 1193
3.0%
11 1278
3.2%
ValueCountFrequency (%)
90 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 1
< 0.1%

initial_list_status
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False 39717
100.0%
2023-10-02T15:01:36.830282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

out_prncp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1137
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.227887
Minimum0
Maximum6311.47
Zeros38577
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:36.876865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6311.47
Range6311.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.17284
Coefficient of variation (CV)7.3236055
Kurtosis97.658555
Mean51.227887
Median Absolute Deviation (MAD)0
Skewness9.22673
Sum2034618
Variance140754.66
MonotonicityNot monotonic
2023-10-02T15:01:36.935767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38577
97.1%
1972.6 2
 
< 0.1%
827.13 2
 
< 0.1%
2277.11 2
 
< 0.1%
2963.24 2
 
< 0.1%
2000.47 1
 
< 0.1%
241.36 1
 
< 0.1%
581.29 1
 
< 0.1%
992.48 1
 
< 0.1%
1279.63 1
 
< 0.1%
Other values (1127) 1127
 
2.8%
ValueCountFrequency (%)
0 38577
97.1%
10.26 1
 
< 0.1%
11.91 1
 
< 0.1%
13.28 1
 
< 0.1%
19.12 1
 
< 0.1%
27.41 1
 
< 0.1%
40.65 1
 
< 0.1%
50.46 1
 
< 0.1%
53 1
 
< 0.1%
57.67 1
 
< 0.1%
ValueCountFrequency (%)
6311.47 1
< 0.1%
6308.37 1
< 0.1%
6307.37 1
< 0.1%
6307.15 1
< 0.1%
6219.16 1
< 0.1%
6219.11 1
< 0.1%
6182.86 1
< 0.1%
6071.68 1
< 0.1%
6034.37 1
< 0.1%
6027.7 1
< 0.1%

out_prncp_inv
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1138
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.989768
Minimum0
Maximum6307.37
Zeros38577
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:36.992617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6307.37
Range6307.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation373.82446
Coefficient of variation (CV)7.3313622
Kurtosis98.040553
Mean50.989768
Median Absolute Deviation (MAD)0
Skewness9.2437655
Sum2025160.6
Variance139744.72
MonotonicityNot monotonic
2023-10-02T15:01:37.051192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38577
97.1%
1972.6 2
 
< 0.1%
1664.64 2
 
< 0.1%
827.13 2
 
< 0.1%
1863.21 1
 
< 0.1%
2398.86 1
 
< 0.1%
285.12 1
 
< 0.1%
804.39 1
 
< 0.1%
1588.87 1
 
< 0.1%
1054.65 1
 
< 0.1%
Other values (1128) 1128
 
2.8%
ValueCountFrequency (%)
0 38577
97.1%
10.26 1
 
< 0.1%
11.91 1
 
< 0.1%
13.28 1
 
< 0.1%
19.09 1
 
< 0.1%
27.41 1
 
< 0.1%
40.65 1
 
< 0.1%
50.46 1
 
< 0.1%
53 1
 
< 0.1%
57.67 1
 
< 0.1%
ValueCountFrequency (%)
6307.37 1
< 0.1%
6306.96 1
< 0.1%
6298.11 1
< 0.1%
6276.75 1
< 0.1%
6219.16 1
< 0.1%
6183.55 1
< 0.1%
6182.86 1
< 0.1%
6067.33 1
< 0.1%
6034.37 1
< 0.1%
6027.7 1
< 0.1%

total_pymnt
Real number (ℝ)

HIGH CORRELATION 

Distinct37850
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.597
Minimum0
Maximum58563.68
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.213015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1887.957
Q15576.93
median9899.6403
Q316534.433
95-th percentile30245.119
Maximum58563.68
Range58563.68
Interquartile range (IQR)10957.503

Descriptive statistics

Standard deviation9042.0408
Coefficient of variation (CV)0.74398066
Kurtosis1.9858942
Mean12153.597
Median Absolute Deviation (MAD)5016.7567
Skewness1.3398574
Sum4.8270439 × 108
Variance81758501
MonotonicityNot monotonic
2023-10-02T15:01:37.272964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.56943 26
 
0.1%
0 16
 
< 0.1%
11784.23223 16
 
< 0.1%
10956.77596 16
 
< 0.1%
5478.387981 15
 
< 0.1%
13148.13786 15
 
< 0.1%
13435.90021 13
 
< 0.1%
5557.025543 13
 
< 0.1%
13263.95464 12
 
< 0.1%
8760.210626 11
 
< 0.1%
Other values (37840) 39564
99.6%
ValueCountFrequency (%)
0 16
< 0.1%
33.73 1
 
< 0.1%
35.71 1
 
< 0.1%
44.92 2
 
< 0.1%
44.96 1
 
< 0.1%
61.71 1
 
< 0.1%
62.86 1
 
< 0.1%
66.77 1
 
< 0.1%
67.32 1
 
< 0.1%
69.64 1
 
< 0.1%
ValueCountFrequency (%)
58563.67993 1
< 0.1%
58480.13992 1
< 0.1%
57835.27991 1
< 0.1%
56849.26986 1
< 0.1%
56662.58994 1
< 0.1%
56199.43995 1
< 0.1%
55906.9499 1
< 0.1%
55768.77995 1
< 0.1%
55368.40995 1
< 0.1%
55138.99996 1
< 0.1%

total_pymnt_inv
Real number (ℝ)

HIGH CORRELATION 

Distinct37518
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11567.149
Minimum0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.332866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1420.408
Q15112.31
median9287.15
Q315798.81
95-th percentile29627.236
Maximum58563.68
Range58563.68
Interquartile range (IQR)10686.5

Descriptive statistics

Standard deviation8942.6726
Coefficient of variation (CV)0.77310948
Kurtosis2.0297585
Mean11567.149
Median Absolute Deviation (MAD)4939.58
Skewness1.3548376
Sum4.5941246 × 108
Variance79971393
MonotonicityNot monotonic
2023-10-02T15:01:37.393232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165
 
0.4%
6514.52 16
 
< 0.1%
5478.39 14
 
< 0.1%
13148.14 14
 
< 0.1%
11196.57 12
 
< 0.1%
10956.78 12
 
< 0.1%
6717.95 12
 
< 0.1%
5557.03 11
 
< 0.1%
7328.92 11
 
< 0.1%
13517.36 11
 
< 0.1%
Other values (37508) 39439
99.3%
ValueCountFrequency (%)
0 165
0.4%
0.54 1
 
< 0.1%
12.65 1
 
< 0.1%
18.97 1
 
< 0.1%
21.6 1
 
< 0.1%
25.18 1
 
< 0.1%
26.19 1
 
< 0.1%
33.73 1
 
< 0.1%
33.99 1
 
< 0.1%
35.71 1
 
< 0.1%
ValueCountFrequency (%)
58563.68 1
< 0.1%
58438.37 1
< 0.1%
57628.73 1
< 0.1%
56622.12 1
< 0.1%
56515.16 1
< 0.1%
55867.02 1
< 0.1%
55579.28 1
< 0.1%
55066.92 1
< 0.1%
54675.68 1
< 0.1%
54315.94 1
< 0.1%

total_rec_prncp
Real number (ℝ)

HIGH CORRELATION 

Distinct7976
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9793.3488
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.453750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1339.842
Q14600
median8000
Q313653.26
95-th percentile24999.982
Maximum35000.02
Range35000.02
Interquartile range (IQR)9053.26

Descriptive statistics

Standard deviation7065.5221
Coefficient of variation (CV)0.7214613
Kurtosis1.1033555
Mean9793.3488
Median Absolute Deviation (MAD)4000
Skewness1.1182545
Sum3.8896243 × 108
Variance49921603
MonotonicityNot monotonic
2023-10-02T15:01:37.514542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2293
 
5.8%
12000 1805
 
4.5%
5000 1702
 
4.3%
6000 1637
 
4.1%
15000 1400
 
3.5%
8000 1318
 
3.3%
20000 1059
 
2.7%
4000 956
 
2.4%
3000 883
 
2.2%
7000 851
 
2.1%
Other values (7966) 25813
65.0%
ValueCountFrequency (%)
0 74
0.2%
21.21 1
 
< 0.1%
21.93 1
 
< 0.1%
22.24 1
 
< 0.1%
22.5 1
 
< 0.1%
24.87 1
 
< 0.1%
30.32 1
 
< 0.1%
32.51 1
 
< 0.1%
34.5 1
 
< 0.1%
35.14 1
 
< 0.1%
ValueCountFrequency (%)
35000.02 2
 
< 0.1%
35000.01 1
 
< 0.1%
35000 363
0.9%
34999.99 5
 
< 0.1%
34999.98 1
 
< 0.1%
34999.97 1
 
< 0.1%
34911.47 1
 
< 0.1%
34800 1
 
< 0.1%
34793.43 1
 
< 0.1%
34675 1
 
< 0.1%

total_rec_int
Real number (ℝ)

HIGH CORRELATION 

Distinct35148
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2263.6632
Minimum0
Maximum23563.68
Zeros71
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.576581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.168
Q1662.18
median1348.91
Q32833.4
95-th percentile7575.812
Maximum23563.68
Range23563.68
Interquartile range (IQR)2171.22

Descriptive statistics

Standard deviation2608.112
Coefficient of variation (CV)1.1521643
Kurtosis9.6882784
Mean2263.6632
Median Absolute Deviation (MAD)866.01
Skewness2.6687472
Sum89905910
Variance6802248
MonotonicityNot monotonic
2023-10-02T15:01:37.636450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
0.2%
1196.57 26
 
0.1%
514.52 19
 
< 0.1%
956.78 17
 
< 0.1%
1784.23 17
 
< 0.1%
1148.14 17
 
< 0.1%
717.95 17
 
< 0.1%
478.39 16
 
< 0.1%
1907.35 14
 
< 0.1%
1435.9 13
 
< 0.1%
Other values (35138) 39490
99.4%
ValueCountFrequency (%)
0 71
0.2%
6.22 1
 
< 0.1%
6.27 1
 
< 0.1%
7.19 1
 
< 0.1%
7.2 2
 
< 0.1%
8.23 1
 
< 0.1%
9.34 1
 
< 0.1%
9.49 1
 
< 0.1%
9.58 2
 
< 0.1%
10.26 1
 
< 0.1%
ValueCountFrequency (%)
23563.68 1
< 0.1%
23506.56 1
< 0.1%
23480.14 1
< 0.1%
22835.28 1
< 0.1%
22716.42 1
< 0.1%
22594.16 1
< 0.1%
22593.34 1
< 0.1%
22593.04 1
< 0.1%
22587.51 1
< 0.1%
22422.33 1
< 0.1%

total_rec_late_fee
Real number (ℝ)

ZEROS 

Distinct1356
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3630152
Minimum0
Maximum180.2
Zeros37671
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.693416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.924199
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2899793
Coefficient of variation (CV)5.3484211
Kurtosis100.85154
Mean1.3630152
Median Absolute Deviation (MAD)0
Skewness8.429536
Sum54134.875
Variance53.143798
MonotonicityNot monotonic
2023-10-02T15:01:37.757455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37671
94.8%
15 255
 
0.6%
15.00000001 58
 
0.1%
30 55
 
0.1%
15.00000002 47
 
0.1%
14.99999999 40
 
0.1%
14.99999998 33
 
0.1%
15.00000003 32
 
0.1%
14.99999997 25
 
0.1%
15.00000004 25
 
0.1%
Other values (1346) 1476
 
3.7%
ValueCountFrequency (%)
0 37671
94.8%
0.01 1
 
< 0.1%
0.060799751 1
 
< 0.1%
0.073787104 1
 
< 0.1%
0.101704562 1
 
< 0.1%
0.139999999 1
 
< 0.1%
0.180082904 1
 
< 0.1%
0.18477362 1
 
< 0.1%
0.27 1
 
< 0.1%
0.302036553 1
 
< 0.1%
ValueCountFrequency (%)
180.2 1
< 0.1%
166.4297107 1
< 0.1%
165.69 1
< 0.1%
146.6000003 1
< 0.1%
146.04 1
< 0.1%
134.0700007 1
< 0.1%
130.5970372 1
< 0.1%
130.47 1
< 0.1%
127.7878136 1
< 0.1%
121.93 1
< 0.1%

recoveries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4040
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.221624
Minimum0
Maximum29623.35
Zeros35499
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.818599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile362.418
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation688.74477
Coefficient of variation (CV)7.233071
Kurtosis379.37758
Mean95.221624
Median Absolute Deviation (MAD)0
Skewness16.519378
Sum3781917.2
Variance474369.36
MonotonicityNot monotonic
2023-10-02T15:01:37.877782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35499
89.4%
11.29 4
 
< 0.1%
10.4 4
 
< 0.1%
10.66 3
 
< 0.1%
44.92 3
 
< 0.1%
10.07 3
 
< 0.1%
16.27 3
 
< 0.1%
13 3
 
< 0.1%
164.81 3
 
< 0.1%
16.5 3
 
< 0.1%
Other values (4030) 4189
 
10.5%
ValueCountFrequency (%)
0 35499
89.4%
6.3 1
 
< 0.1%
6.31 1
 
< 0.1%
8.19 1
 
< 0.1%
8.36 1
 
< 0.1%
8.41 1
 
< 0.1%
8.46 1
 
< 0.1%
8.56 1
 
< 0.1%
8.71 1
 
< 0.1%
8.88 1
 
< 0.1%
ValueCountFrequency (%)
29623.35 1
< 0.1%
22943.37 1
< 0.1%
21810.31 1
< 0.1%
20006.53 1
< 0.1%
19915.67 1
< 0.1%
19508.26 1
< 0.1%
18694.32 1
< 0.1%
16560.06 1
< 0.1%
16502.69 1
< 0.1%
16268.35 1
< 0.1%

collection_recovery_fee
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2616
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.406112
Minimum0
Maximum7002.19
Zeros35935
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:37.940776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.152
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.67159
Coefficient of variation (CV)11.983738
Kurtosis821.30066
Mean12.406112
Median Absolute Deviation (MAD)0
Skewness25.029418
Sum492733.55
Variance22103.243
MonotonicityNot monotonic
2023-10-02T15:01:38.011531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35935
90.5%
2 12
 
< 0.1%
1.2 10
 
< 0.1%
3.71 9
 
< 0.1%
1.88 8
 
< 0.1%
1.21 8
 
< 0.1%
0.8 8
 
< 0.1%
2.02 8
 
< 0.1%
1.69 8
 
< 0.1%
1.6 8
 
< 0.1%
Other values (2606) 3703
 
9.3%
ValueCountFrequency (%)
0 35935
90.5%
0.063 1
 
< 0.1%
0.074500001 1
 
< 0.1%
0.134799995 1
 
< 0.1%
0.1393 1
 
< 0.1%
0.16 1
 
< 0.1%
0.1952 1
 
< 0.1%
0.197899999 1
 
< 0.1%
0.200700001 1
 
< 0.1%
0.2147 1
 
< 0.1%
ValueCountFrequency (%)
7002.19 1
< 0.1%
6972.59 1
< 0.1%
6543.04 1
< 0.1%
5774.8 1
< 0.1%
5602.72 1
< 0.1%
5569.92 1
< 0.1%
5216.74 1
< 0.1%
5036.01 1
< 0.1%
4902.08 1
< 0.1%
4900.75 1
< 0.1%
Distinct101
Distinct (%)0.3%
Missing71
Missing (%)0.2%
Memory size310.4 KiB
2023-10-02T15:01:38.220193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters237876
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-15
2nd rowApr-13
3rd rowJun-14
4th rowJan-15
5th rowMay-16
ValueCountFrequency (%)
may-16 1256
 
3.2%
mar-13 1026
 
2.6%
dec-14 945
 
2.4%
may-13 907
 
2.3%
feb-13 869
 
2.2%
apr-13 851
 
2.1%
mar-12 844
 
2.1%
jan-14 832
 
2.1%
aug-14 832
 
2.1%
aug-12 832
 
2.1%
Other values (91) 30452
76.8%
2023-10-02T15:01:38.498376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 43946
18.5%
- 39646
16.7%
a 11087
 
4.7%
e 9738
 
4.1%
3 9458
 
4.0%
u 9401
 
4.0%
4 9269
 
3.9%
J 9200
 
3.9%
2 8904
 
3.7%
M 8046
 
3.4%
Other values (22) 79181
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79292
33.3%
Lowercase Letter 79292
33.3%
Dash Punctuation 39646
16.7%
Uppercase Letter 39646
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11087
14.0%
e 9738
12.3%
u 9401
11.9%
r 6965
8.8%
c 6783
8.6%
p 6219
7.8%
n 5974
7.5%
y 4285
 
5.4%
t 3271
 
4.1%
g 3242
 
4.1%
Other values (4) 12327
15.5%
Decimal Number
ValueCountFrequency (%)
1 43946
55.4%
3 9458
 
11.9%
4 9269
 
11.7%
2 8904
 
11.2%
0 2544
 
3.2%
5 2431
 
3.1%
6 2044
 
2.6%
9 559
 
0.7%
8 137
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
J 9200
23.2%
M 8046
20.3%
A 6446
16.3%
D 3512
 
8.9%
O 3271
 
8.3%
F 3211
 
8.1%
S 3015
 
7.6%
N 2945
 
7.4%
Dash Punctuation
ValueCountFrequency (%)
- 39646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118938
50.0%
Latin 118938
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11087
 
9.3%
e 9738
 
8.2%
u 9401
 
7.9%
J 9200
 
7.7%
M 8046
 
6.8%
r 6965
 
5.9%
c 6783
 
5.7%
A 6446
 
5.4%
p 6219
 
5.2%
n 5974
 
5.0%
Other values (12) 39079
32.9%
Common
ValueCountFrequency (%)
1 43946
36.9%
- 39646
33.3%
3 9458
 
8.0%
4 9269
 
7.8%
2 8904
 
7.5%
0 2544
 
2.1%
5 2431
 
2.0%
6 2044
 
1.7%
9 559
 
0.5%
8 137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 43946
18.5%
- 39646
16.7%
a 11087
 
4.7%
e 9738
 
4.1%
3 9458
 
4.0%
u 9401
 
4.0%
4 9269
 
3.9%
J 9200
 
3.9%
2 8904
 
3.7%
M 8046
 
3.4%
Other values (22) 79181
33.3%

last_pymnt_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct34930
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.8262
Minimum0
Maximum36115.2
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2023-10-02T15:01:38.584831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.34
Q1218.68
median546.14
Q33293.16
95-th percentile12183.944
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.48

Descriptive statistics

Standard deviation4447.136
Coefficient of variation (CV)1.6601062
Kurtosis8.8678197
Mean2678.8262
Median Absolute Deviation (MAD)449.45
Skewness2.7121222
Sum1.0639494 × 108
Variance19777019
MonotonicityNot monotonic
2023-10-02T15:01:38.654487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
0.2%
276.06 21
 
0.1%
200 17
 
< 0.1%
50 16
 
< 0.1%
100 15
 
< 0.1%
400 12
 
< 0.1%
773.44 12
 
< 0.1%
150 11
 
< 0.1%
786.01 11
 
< 0.1%
500 11
 
< 0.1%
Other values (34920) 39517
99.5%
ValueCountFrequency (%)
0 74
0.2%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.28 1
 
< 0.1%
ValueCountFrequency (%)
36115.2 1
< 0.1%
35613.68 1
< 0.1%
35596.41 1
< 0.1%
35479.89 1
< 0.1%
35471.86 1
< 0.1%
35395.59 1
< 0.1%
35339.05 1
< 0.1%
35337.09 1
< 0.1%
35322.96 1
< 0.1%
35322.6 1
< 0.1%

next_pymnt_d
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing38577
Missing (%)97.1%
Memory size310.4 KiB
Jun-16
1125 
Jul-16
 
15

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6840
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJun-16
2nd rowJun-16
3rd rowJun-16
4th rowJun-16
5th rowJun-16

Common Values

ValueCountFrequency (%)
Jun-16 1125
 
2.8%
Jul-16 15
 
< 0.1%
(Missing) 38577
97.1%

Length

2023-10-02T15:01:38.715713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:38.759423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jun-16 1125
98.7%
jul-16 15
 
1.3%

Most occurring characters

ValueCountFrequency (%)
J 1140
16.7%
u 1140
16.7%
- 1140
16.7%
1 1140
16.7%
6 1140
16.7%
n 1125
16.4%
l 15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2280
33.3%
Decimal Number 2280
33.3%
Uppercase Letter 1140
16.7%
Dash Punctuation 1140
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1140
50.0%
n 1125
49.3%
l 15
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1140
50.0%
6 1140
50.0%
Uppercase Letter
ValueCountFrequency (%)
J 1140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3420
50.0%
Common 3420
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 1140
33.3%
u 1140
33.3%
n 1125
32.9%
l 15
 
0.4%
Common
ValueCountFrequency (%)
- 1140
33.3%
1 1140
33.3%
6 1140
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J 1140
16.7%
u 1140
16.7%
- 1140
16.7%
1 1140
16.7%
6 1140
16.7%
n 1125
16.4%
l 15
 
0.2%
Distinct106
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size310.4 KiB
2023-10-02T15:01:38.931081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238290
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMay-16
2nd rowSep-13
3rd rowMay-16
4th rowApr-16
5th rowMay-16
ValueCountFrequency (%)
may-16 10308
26.0%
apr-16 2547
 
6.4%
mar-16 1123
 
2.8%
feb-13 843
 
2.1%
feb-16 736
 
1.9%
jan-16 657
 
1.7%
dec-15 647
 
1.6%
mar-13 577
 
1.5%
mar-14 564
 
1.4%
dec-14 562
 
1.4%
Other values (96) 21151
53.3%
2023-10-02T15:01:39.155767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 41601
17.5%
- 39715
16.7%
a 17601
 
7.4%
M 15523
 
6.5%
6 15371
 
6.5%
y 12231
 
5.1%
r 7664
 
3.2%
e 7600
 
3.2%
p 6483
 
2.7%
A 6411
 
2.7%
Other values (23) 68090
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79430
33.3%
Lowercase Letter 79430
33.3%
Dash Punctuation 39715
16.7%
Uppercase Letter 39715
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17601
22.2%
y 12231
15.4%
r 7664
9.6%
e 7600
9.6%
p 6483
 
8.2%
u 5856
 
7.4%
c 4475
 
5.6%
n 3834
 
4.8%
b 3075
 
3.9%
o 2225
 
2.8%
Other values (4) 8386
10.6%
Decimal Number
ValueCountFrequency (%)
1 41601
52.4%
6 15371
 
19.4%
4 6255
 
7.9%
5 5502
 
6.9%
3 5164
 
6.5%
2 4079
 
5.1%
0 1153
 
1.5%
9 228
 
0.3%
8 41
 
0.1%
7 36
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 15523
39.1%
A 6411
16.1%
J 5895
 
14.8%
F 3075
 
7.7%
D 2414
 
6.1%
N 2225
 
5.6%
S 2111
 
5.3%
O 2061
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 39715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119145
50.0%
Latin 119145
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17601
14.8%
M 15523
13.0%
y 12231
10.3%
r 7664
 
6.4%
e 7600
 
6.4%
p 6483
 
5.4%
A 6411
 
5.4%
J 5895
 
4.9%
u 5856
 
4.9%
c 4475
 
3.8%
Other values (12) 29406
24.7%
Common
ValueCountFrequency (%)
1 41601
34.9%
- 39715
33.3%
6 15371
 
12.9%
4 6255
 
5.2%
5 5502
 
4.6%
3 5164
 
4.3%
2 4079
 
3.4%
0 1153
 
1.0%
9 228
 
0.2%
8 41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 41601
17.5%
- 39715
16.7%
a 17601
 
7.4%
M 15523
 
6.5%
6 15371
 
6.5%
y 12231
 
5.1%
r 7664
 
3.2%
e 7600
 
3.2%
p 6483
 
2.7%
A 6411
 
2.7%
Other values (23) 68090
28.6%

collections_12_mths_ex_med
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.4 KiB
0.0
39661 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters118983
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 39661
99.9%
(Missing) 56
 
0.1%

Length

2023-10-02T15:01:39.233085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.273471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 39661
100.0%

Most occurring characters

ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79322
66.7%
Other Punctuation 39661
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79322
100.0%
Other Punctuation
ValueCountFrequency (%)
. 39661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

mths_since_last_major_derog
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

policy_code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
1
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 39717
100.0%

Length

2023-10-02T15:01:39.315672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.355616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 39717
100.0%

Most occurring characters

ValueCountFrequency (%)
1 39717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39717
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39717
100.0%

application_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
INDIVIDUAL
39717 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 39717
100.0%

Length

2023-10-02T15:01:39.396965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.439561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 39717
100.0%

Most occurring characters

ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 397170
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397170
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

annual_inc_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

dti_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

verification_status_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

acc_now_delinq
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39717
100.0%

Length

2023-10-02T15:01:39.481502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.522358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39717
100.0%

Most occurring characters

ValueCountFrequency (%)
0 39717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39717
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39717
100.0%

tot_coll_amt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

tot_cur_bal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_acc_6m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_6m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_il_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_rcnt_il
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bal_il
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

il_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_rv_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

open_rv_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

max_bal_bc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

all_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_rev_hi_lim
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

inq_fi
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_cu_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

inq_last_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

acc_open_past_24mths
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

avg_cur_bal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

bc_open_to_buy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

bc_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

chargeoff_within_12_mths
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.4 KiB
0.0
39661 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters118983
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 39661
99.9%
(Missing) 56
 
0.1%

Length

2023-10-02T15:01:39.563476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.602443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 39661
100.0%

Most occurring characters

ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79322
66.7%
Other Punctuation 39661
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79322
100.0%
Other Punctuation
ValueCountFrequency (%)
. 39661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79322
66.7%
. 39661
33.3%

delinq_amnt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
39717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39717
100.0%

Length

2023-10-02T15:01:39.644912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.684263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39717
100.0%

Most occurring characters

ValueCountFrequency (%)
0 39717
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39717
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39717
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39717
100.0%

mo_sin_old_il_acct
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_old_rev_tl_op
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_rcnt_rev_tl_op
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mo_sin_rcnt_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mort_acc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_bc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_bc_dlq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_inq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

mths_since_recent_revol_delinq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_accts_ever_120_pd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_actv_bc_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_actv_rev_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_bc_sats
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_bc_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_il_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_op_rev_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_rev_accts
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_rev_tl_bal_gt_0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_sats
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_120dpd_2m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_30dpd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_90g_dpd_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

num_tl_op_past_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

pct_tl_nvr_dlq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

percent_bc_gt_75
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

pub_rec_bankruptcies
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size310.4 KiB
0.0
37339 
1.0
 
1674
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37339
94.0%
1.0 1674
 
4.2%
2.0 7
 
< 0.1%
(Missing) 697
 
1.8%

Length

2023-10-02T15:01:39.726695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.768605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37339
95.7%
1.0 1674
 
4.3%
2.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78040
66.7%
Other Punctuation 39020
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76359
97.8%
1 1674
 
2.1%
2 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 39020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

tax_liens
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing39
Missing (%)0.1%
Memory size310.4 KiB
0.0
39678 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters119034
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 39678
99.9%
(Missing) 39
 
0.1%

Length

2023-10-02T15:01:39.811934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T15:01:39.852511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 39678
100.0%

Most occurring characters

ValueCountFrequency (%)
0 79356
66.7%
. 39678
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79356
66.7%
Other Punctuation 39678
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79356
100.0%
Other Punctuation
ValueCountFrequency (%)
. 39678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79356
66.7%
. 39678
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79356
66.7%
. 39678
33.3%

tot_hi_cred_lim
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bal_ex_mort
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_bc_limit
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

total_il_high_credit_limit
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing39717
Missing (%)100.0%
Memory size310.4 KiB

Interactions

2023-10-02T15:01:26.213145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:57.472716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.704591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.926536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.107577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.312738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.582913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.753594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.012355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:07.157980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.313000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.590317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.714713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.929225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:13.032999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.368566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.489023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.624093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.909634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:19.090188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.260777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.543711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.648309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.905634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:25.037426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.257977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:57.551242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.747256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.972036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.151174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.357580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.626196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.802405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.056819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:07.202193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.461577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.632123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.757148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.969960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:13.084230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.410828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.532865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.669691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.952909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:19.136191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.306200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.585399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.691701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.948312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:25.083396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.306726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:57.620560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.791380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:00.017543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.199207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.404773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.670829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.849516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.101208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-10-02T15:01:07.995980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.256719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.398796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.624125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.712598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:13.929542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.172159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.303007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.583275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.756125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:19.935522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.206221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.335932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.580517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.716673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:25.879175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.202528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.430115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.560037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:00.822779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.994448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.298580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.466433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.737106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.876663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.041264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.303907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.443713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.665689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.758169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:13.977764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.218970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.349374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.629452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.805151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:19.981859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.254642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.379743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.626555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.763185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:25.926124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.252071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.477836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.697266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:00.870849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.042601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.346959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.514346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.784037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.923314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.087409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.351848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.492218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.711587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.803212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.028607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.264824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.397170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.675837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.854849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.029588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.304031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.426056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.674425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.809270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:25.975345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.294996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.520722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.739616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:00.916172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.086956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.390566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.559909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.826462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:06.965983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.128393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.396990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.532993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.753358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.845442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.072111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.305931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.438584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.720167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.898269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.072724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.350296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.466024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.716449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.851491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.020135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.345350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.568253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.787785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:00.964577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.134303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.439919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.608685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.873063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:07.013112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.175160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.445275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.579729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.795491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.890724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.121715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.352395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.485275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.768147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.946853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.120123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.398366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.512275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.762113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.898871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.068442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.391507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.612234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.833347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.011734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.206721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.485868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.657095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.917366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:07.058860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.219588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.493315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.623089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.839395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.935804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.168415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.396411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.529349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.814794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:18.993616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.165332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.445749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.556158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.808893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.942472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.115133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:27.441329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:58.658639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:00:59.879963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:01.059909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:02.263316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:03.534655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:04.705356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:05.964312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:07.104570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:08.267983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:09.541661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:10.667558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:11.880930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:12.981933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:14.217917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:15.443246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:16.576615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:17.860713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:19.043091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:20.213498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:21.493948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:22.603191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:23.856641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:24.990158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T15:01:26.162425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-02T15:01:39.905816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idmember_idloan_amntfunded_amntfunded_amnt_invinstallmentannual_incdtidelinq_2yrsinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accrevol_baltotal_accout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_amnttermgradesub_gradeemp_lengthhome_ownershipverification_statusloan_statuspurposeaddr_statepub_recnext_pymnt_dpub_rec_bankruptcies
id1.0000.9990.1110.1210.2330.0760.0440.087-0.010-0.0380.0940.5930.0310.0490.0520.1880.1880.1030.2010.0820.106-0.0520.0520.0380.1210.2990.0490.0680.0560.0830.2360.1480.0730.0910.0170.0120.071
member_id0.9991.0000.1100.1210.2320.0750.0430.088-0.009-0.0380.0940.5940.0310.0490.0520.1870.1870.1030.2000.0810.105-0.0520.0520.0380.1210.3110.0510.0700.0560.0820.2530.1530.0740.0990.0190.0250.044
loan_amnt0.1110.1101.0000.9910.9350.9580.4290.074-0.037-0.0030.0220.0220.2080.3990.2750.1230.1230.8870.8380.8370.778-0.0210.0440.0340.4490.3640.1370.1250.0540.0880.3130.1080.1160.0190.0230.0000.028
funded_amnt0.1210.1210.9911.0000.9460.9720.4250.074-0.037-0.0040.0230.0170.2060.3950.2700.1240.1240.8960.8490.8460.785-0.0210.0440.0340.4530.3440.1370.1230.0540.0850.3060.1060.1150.0160.0230.0550.027
funded_amnt_inv0.2330.2320.9350.9461.0000.9100.4020.083-0.042-0.0170.0950.4560.1920.3690.2620.1330.1330.8600.9060.8120.757-0.0360.0360.0240.4440.3670.1310.1170.0580.0840.3140.1090.1090.0260.0240.0560.030
installment0.0760.0750.9580.9720.9101.0000.4220.067-0.0230.0000.010-0.0200.1970.3950.2470.0700.0700.8710.8170.8350.734-0.0170.0260.0210.4390.1480.1370.1230.0450.0700.2720.0600.1130.0150.0220.0000.028
annual_inc0.0440.0430.4290.4250.4020.4221.000-0.1030.0360.033-0.0060.0330.3050.3970.4300.0380.0380.4060.3860.4000.304-0.033-0.056-0.0570.2450.0000.0000.0000.0060.0000.0000.0000.0000.0160.0000.0000.000
dti0.0870.0880.0740.0740.0830.067-0.1031.000-0.0380.0130.0650.1710.3050.3320.2400.0350.0350.0640.0710.0420.120-0.0070.0280.0210.0130.0830.0630.0570.0190.0240.0730.0460.0820.0320.0150.0000.015
delinq_2yrs-0.010-0.009-0.037-0.037-0.042-0.0230.036-0.0381.0000.011-0.722-0.0420.006-0.0830.071-0.002-0.002-0.029-0.036-0.0420.0190.0420.0180.020-0.0220.0050.0500.0460.0000.0040.0000.0070.0200.0000.0350.0000.014
inq_last_6mths-0.038-0.038-0.003-0.004-0.0170.0000.0330.0130.0111.0000.003-0.0230.094-0.0260.104-0.013-0.013-0.027-0.037-0.0420.0200.0320.0500.0440.0090.0420.0790.0850.0010.0410.0110.0530.0390.0530.0220.0000.024
mths_since_last_delinq0.0940.0940.0220.0230.0950.010-0.0060.065-0.7220.0031.0000.5140.0320.0780.0120.0040.0040.0190.0860.0210.012-0.0370.0070.0020.0260.0410.0380.0340.0320.0250.0550.0250.0040.0430.0310.0000.017
mths_since_last_record0.5930.5940.0220.0170.456-0.0200.0330.171-0.042-0.0230.5141.0000.0540.0290.1170.0720.072-0.0070.396-0.0350.063-0.0660.0320.0090.0820.2980.0850.0950.0930.0660.3380.0620.0620.0990.5261.0000.658
open_acc0.0310.0310.2080.2060.1920.1970.3050.3050.0060.0940.0320.0541.0000.3900.6890.0290.0290.1870.1750.1830.147-0.039-0.023-0.0310.0990.0540.0760.0850.0330.1060.0720.0190.0480.0260.0000.0620.000
revol_bal0.0490.0490.3990.3950.3690.3950.3970.332-0.083-0.0260.0780.0290.3901.0000.3790.0530.0530.3600.3370.3350.342-0.033-0.001-0.0090.1610.0960.0490.0460.0550.1090.1330.0370.0630.0150.0340.0000.042
total_acc0.0520.0520.2750.2700.2620.2470.4300.2400.0710.1040.0120.1170.6890.3791.0000.0260.0260.2410.2350.2420.154-0.048-0.044-0.0550.1780.1000.0500.0500.0740.1690.1010.0320.0480.0350.0220.0000.029
out_prncp0.1880.1870.1230.1240.1330.0700.0380.035-0.002-0.0130.0040.0720.0290.0530.0261.0001.0000.1580.1640.1230.223-0.005-0.059-0.056-0.0730.2480.0510.0560.0180.0180.0740.6160.0130.0000.0110.0000.060
out_prncp_inv0.1880.1870.1230.1240.1330.0700.0380.035-0.002-0.0130.0040.0720.0290.0530.0261.0001.0000.1580.1640.1230.223-0.005-0.059-0.056-0.0730.2470.0520.0560.0180.0180.0740.6150.0130.0000.0110.0000.060
total_pymnt0.1030.1030.8870.8960.8600.8710.4060.064-0.029-0.0270.019-0.0070.1870.3600.2410.1580.1581.0000.9560.9770.823-0.054-0.207-0.1970.4960.3350.1510.1340.0470.0770.2830.2380.1000.0140.0250.1220.035
total_pymnt_inv0.2010.2000.8380.8490.9060.8170.3860.071-0.036-0.0370.0860.3960.1750.3370.2350.1640.1640.9561.0000.9330.790-0.066-0.203-0.1940.4850.3500.1440.1280.0510.0770.2860.2390.0960.0170.0250.1770.039
total_rec_prncp0.0820.0810.8370.8460.8120.8350.4000.042-0.042-0.0420.021-0.0350.1830.3350.2420.1230.1230.9770.9331.0000.721-0.085-0.323-0.3080.5370.2520.1090.0990.0450.0800.2700.3290.1000.0190.0320.0980.038
total_rec_int0.1060.1050.7780.7850.7570.7340.3040.1200.0190.0200.0120.0630.1470.3420.1540.2230.2230.8230.7900.7211.0000.0250.0170.0060.2460.5420.2340.2020.0420.0580.2540.2350.0690.0000.0120.0000.059
total_rec_late_fee-0.052-0.052-0.021-0.021-0.036-0.017-0.033-0.0070.0420.032-0.037-0.066-0.039-0.033-0.048-0.005-0.005-0.054-0.066-0.0850.0251.0000.1850.189-0.1210.0240.0470.0490.0000.0190.0200.1020.0160.0420.0000.0000.000
recoveries0.0520.0520.0440.0440.0360.026-0.0560.0280.0180.0500.0070.032-0.023-0.001-0.044-0.059-0.059-0.207-0.203-0.3230.0170.1851.0000.950-0.2510.0600.0390.0500.0050.0000.0390.1450.0130.0000.0001.0000.000
collection_recovery_fee0.0380.0380.0340.0340.0240.021-0.0570.0210.0200.0440.0020.009-0.031-0.009-0.055-0.056-0.056-0.197-0.194-0.3080.0060.1890.9501.000-0.2440.0400.0320.0390.0000.0000.0260.1140.0060.0000.0001.0000.000
last_pymnt_amnt0.1210.1210.4490.4530.4440.4390.2450.013-0.0220.0090.0260.0820.0990.1610.178-0.073-0.0730.4960.4850.5370.246-0.121-0.251-0.2441.0000.2380.0730.0680.0290.0520.1490.1750.0440.0000.0071.0000.009
term0.2990.3110.3640.3440.3670.1480.0000.0830.0050.0420.0410.2980.0540.0960.1000.2480.2470.3350.3500.2520.5420.0240.0600.0400.2381.0000.4430.4780.1150.1100.2590.3290.1150.0510.0091.0000.010
grade0.0490.0510.1370.1370.1310.1370.0000.0630.0500.0790.0380.0850.0760.0490.0500.0510.0520.1510.1440.1090.2340.0470.0390.0320.0730.4431.0001.0000.0170.0490.1420.1590.0690.0140.0540.0560.066
sub_grade0.0680.0700.1250.1230.1170.1230.0000.0570.0460.0850.0340.0950.0850.0460.0500.0560.0560.1340.1280.0990.2020.0490.0500.0390.0680.4781.0001.0000.0180.0550.1500.1660.0530.0060.0570.0000.072
emp_length0.0560.0560.0540.0540.0580.0450.0060.0190.0000.0010.0320.0930.0330.0550.0740.0180.0180.0470.0510.0450.0420.0000.0050.0000.0290.1150.0170.0181.0000.1320.0840.0410.0360.0230.0330.0000.042
home_ownership0.0830.0820.0880.0850.0840.0700.0000.0240.0040.0410.0250.0660.1060.1090.1690.0180.0180.0770.0770.0800.0580.0190.0000.0000.0520.1100.0490.0550.1321.0000.0760.0320.1250.1340.0110.0000.021
verification_status0.2360.2530.3130.3060.3140.2720.0000.0730.0000.0110.0550.3380.0720.1330.1010.0740.0740.2830.2860.2700.2540.0200.0390.0260.1490.2590.1420.1500.0840.0761.0000.0700.0970.0390.0070.0290.008
loan_status0.1480.1530.1080.1060.1090.0600.0000.0460.0070.0530.0250.0620.0190.0370.0320.6160.6150.2380.2390.3290.2350.1020.1450.1140.1750.3290.1590.1660.0410.0320.0701.0000.0730.0410.0381.0000.036
purpose0.0730.0740.1160.1150.1090.1130.0000.0820.0200.0390.0040.0620.0480.0630.0480.0130.0130.1000.0960.1000.0690.0160.0130.0060.0440.1150.0690.0530.0360.1250.0970.0731.0000.0320.0160.0000.023
addr_state0.0910.0990.0190.0160.0260.0150.0160.0320.0000.0530.0430.0990.0260.0150.0350.0000.0000.0140.0170.0190.0000.0420.0000.0000.0000.0510.0140.0060.0230.1340.0390.0410.0321.0000.0380.0000.049
pub_rec0.0170.0190.0230.0230.0240.0220.0000.0150.0350.0220.0310.5260.0000.0340.0220.0110.0110.0250.0250.0320.0120.0000.0000.0000.0070.0090.0540.0570.0330.0110.0070.0380.0160.0381.0000.0000.690
next_pymnt_d0.0120.0250.0000.0550.0560.0000.0000.0000.0000.0000.0001.0000.0620.0000.0000.0000.0000.1220.1770.0980.0000.0001.0001.0001.0001.0000.0560.0000.0000.0000.0291.0000.0000.0000.0001.0000.000
pub_rec_bankruptcies0.0710.0440.0280.0270.0300.0280.0000.0150.0140.0240.0170.6580.0000.0420.0290.0600.0600.0350.0390.0380.0590.0000.0000.0000.0090.0100.0660.0720.0420.0210.0080.0360.0230.0490.6900.0001.000

Missing values

2023-10-02T15:01:27.621801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-02T15:01:28.007610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-02T15:01:28.693028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeapplication_typeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
010775011296599500050004975.036 months10.65%162.87BB2NaN10+ yearsRENT24000.0VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>credit_cardComputer860xxAZ27.650Jan-851NaNNaN301364883.70%9f0.000.005863.1551875833.845000.00863.160.000.000.00Jan-15171.62NaNMay-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
110774301314167250025002500.060 months15.27%59.83CC4Ryder< 1 yearRENT30000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>carbike309xxGA1.000Apr-995NaNNaN3016879.40%4f0.000.001008.7100001008.71456.46435.170.00117.081.11Apr-13119.66NaNSep-130.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
210771751313524240024002400.036 months15.96%84.33CC5NaN10+ yearsRENT12252.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175NaNsmall_businessreal estate business606xxIL8.720Nov-012NaNNaN20295698.50%10f0.000.003005.6668443005.672400.00605.670.000.000.00Jun-14649.91NaNMay-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
310768631277178100001000010000.036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49200.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>otherpersonel917xxCA20.000Feb-96135.0NaN100559821%37f0.000.0012231.89000012231.8910000.002214.9216.970.000.00Jan-15357.48NaNApr-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
410753581311748300030003000.060 months12.69%67.79BB5University Medical Group1 yearRENT80000.0Source VerifiedDec-11Currentnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>otherPersonal972xxOR17.940Jan-96038.0NaN1502778353.90%38f524.06524.063513.3300003513.332475.941037.390.000.000.00May-1667.79Jun-16May-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
510752691311441500050005000.036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269NaNweddingMy wedding loan I promise to pay back852xxAZ11.200Nov-043NaNNaN90796328.30%12f0.000.005632.2100005632.215000.00632.210.000.000.00Jan-15161.03NaNJan-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
610696391304742700070007000.060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47004.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1069639Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>debt_consolidationLoan280xxNC23.510Jul-051NaNNaN701772685.60%11f0.000.0010110.84000010110.846985.613125.230.000.000.00May-161313.76NaNMay-160.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
710720531288686300030003000.036 months18.64%109.43EE1MKC Accounting9 yearsRENT48000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1072053Borrower added on 12/16/11 > Downpayment for a car.<br>carCar Downpayment900xxCA5.350Jan-072NaNNaN40822187.50%4f0.000.003939.1352943939.143000.00939.140.000.000.00Jan-15111.34NaNDec-140.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
810717951306957560056005600.060 months21.28%152.39FF2NaN4 yearsOWN40000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071795Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) <br>My score is not A+ because I own my home and have no mortgage.<br>small_businessExpand Business & Buy Debt Portfolio958xxCA5.550Apr-042NaNNaN110521032.60%13f0.000.00646.020000646.02162.02294.940.00189.062.09Apr-12152.39NaNAug-120.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
910715701306721537553755350.060 months12.69%121.45BB5Starbucks< 1 yearRENT15000.0VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071570Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car.<br><br> Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.<br>otherBuilding my credit history.774xxTX18.080Sep-040NaNNaN20927936.50%3f0.000.001476.1900001469.34673.48533.420.00269.292.52Nov-12121.45NaNMar-130.0NaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeapplication_typeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
39707926669266150005000525.036 months9.33%159.77BB3Stark and Roth Inc2 yearsMORTGAGE180000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92666Need a loan to make some home improvmentshome_improvementhome improvment loan530xxWI11.930Feb-9510.00.01606056839.20%38f0.00.05751.530533603.915000.0751.530.00.00.0Jul-10161.55NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39708925529254250005000375.036 months9.96%161.25BB5Millenium Group4 yearsMORTGAGE48000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92552I would like to pay off my high-interest credit card debts and have a single payment to make every monthdebt_consolidationTito5000333xxFL8.030Aug-9510.00.0602832948.60%6f0.00.05804.732505435.365000.0804.730.00.00.0Jul-10162.07NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39709925339252950005000675.036 months11.22%164.23CC4Self-Employeed< 1 yearOWN80000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92533NaNcredit_cardP's Family Credit Loan537xxWI1.210Jul-9630.044.01512718516.10%29f0.00.05912.052998798.135000.0912.050.00.00.0Jul-10165.17NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39710925079250250005000250.036 months7.43%155.38AA2Rush Univ Med Grp1 yearOWN85000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92507NaNcredit_cardMy Credit Card Loan537xxWI0.310Oct-9700.00.0702160.60%19f0.00.05593.626092279.685000.0593.630.00.00.0Jul-10156.29NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39711924029239050005000700.036 months8.70%158.30BB1A. F. Wolfers, Inc.5 yearsMORTGAGE75000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92402I'd like to shift some credit card debt so it has a lower interest rate.credit_cardReduce Credit Card Debt804xxCO15.550May-9400.00.01006603323%29f0.00.05698.603286797.805000.0698.600.00.00.0Jul-10159.83NaNNov-14NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397129218792174250025001075.036 months8.07%78.42AA4FiSite Research4 yearsMORTGAGE110000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92187Our current gutter system on our home is old and in need of repair. We will be using the borrowed funds to replace the gutter system on our home.home_improvementHome Improvement802xxCO11.330Nov-9000.00.0130727413.10%40f0.00.02822.9692931213.882500.0322.970.00.00.0Jul-1080.90NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39713906659060785008500875.036 months10.28%275.38CC1Squarewave Solutions, Ltd.3 yearsRENT18000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90665The rate of interest and fees incurred by carrying a balance on my credit card are so outrageous at this point that continuing to pay them is patently bad financial thinking. I wish to redirect my efforts at retiring my debt via another more-reasonable means. I have sufficient funds to direct to this end on a monthly basis, and have simply gotten tired of their being gobbled up by interest and fees.credit_cardRetiring credit card debt274xxNC6.401Dec-8615.00.060884726.90%9f0.00.09913.4918221020.518500.01413.490.00.00.0Jul-10281.94NaNJul-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397149039590390500050001325.036 months8.07%156.84AA4NaN< 1 yearMORTGAGE100000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90395NaNdebt_consolidationMBA Loan Consolidation017xxMA2.300Oct-9800.00.0110969819.40%20f0.00.05272.1611281397.125000.0272.160.00.00.0Apr-080.00NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39715903768924350005000650.036 months7.43%155.38AA2NaN< 1 yearMORTGAGE200000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90376NaNotherJAL Loan208xxMD3.720Nov-8800.00.0170856070.70%26f0.00.05174.198551672.665000.0174.200.00.00.0Jan-080.00NaNJun-07NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39716870238699975007500800.036 months13.75%255.43EE2Evergreen Center< 1 yearOWN22000.0Not VerifiedJun-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=87023I plan to consolidate over $7,000 of debt: a combination of credit cards and student loans.debt_consolidationConsolidation Loan027xxMA14.291Oct-03011.00.070417551.50%8f0.00.09195.263334980.837500.01695.260.00.00.0Jun-10256.59NaNJun-10NaNNaN1INDIVIDUALNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN